Iurii Shcherbak

Global metaanalysis of the nonlinear response of soil nitrous oxide (N2O) emissions to fertilizer nitrogen

Significance We clarify the response of the greenhouse gas nitrous oxide (N2O) to nitrogen (N) fertilizer additions, a topic of considerable debate. Previous analyses have used single N-rate experiments to define a linear response to N additions across climate, management, and soil conditions globally. Here, we provide a first quantitative comparison of N2O emissions for all available studies that have used multiple N rates. Results show that a nonlinear emission factor better represents global emission patterns with lower uncertainty, offering more power for balancing the global N2O budget and for designing effective mitigation strategies. Nitrous oxide (N2O) is a potent greenhouse gas (GHG) that also depletes stratospheric ozone. Nitrogen (N) fertilizer rate is the best single predictor of N2O emissions from agricultural soils, which are responsible for ∼50% of the total global anthropogenic flux, but it is a relatively imprecise estimator. Accumulating evidence suggests that the emission response to increasing N input is exponential rather than linear, as assumed by Intergovernmental Panel on Climate Change methodologies. We performed a metaanalysis to test the generalizability of this pattern. From 78 published studies (233 site-years) with at least three N-input levels, we calculated N2O emission factors (EFs) for each nonzero input level as a percentage of N input converted to N2O emissions. We found that the N2O response to N inputs grew significantly faster than linear for synthetic fertilizers and for most crop types. N-fixing crops had a higher rate of change in EF (ΔEF) than others. A higher ΔEF was also evident in soils with carbon >1.5% and soils with pH <7, and where fertilizer was applied only once annually. Our results suggest a general trend of exponentially increasing N2O emissions as N inputs increase to exceed crop needs. Use of this knowledge in GHG inventories should improve assessments of fertilizer-derived N2O emissions, help address disparities in the global N2O budget, and refine the accuracy of N2O mitigation protocols. In low-input systems typical of sub-Saharan Africa, for example, modest N additions will have little impact on estimated N2O emissions, whereas equivalent additions (or reductions) in excessively fertilized systems will have a disproportionately major impact.

How do various maize crop models vary in their responses to climate change factors

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

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.

Response of wheat growth, grain yield and water use to elevated CO2 under a Free-Air CO2 Enrichment (FACE) experiment and modelling in a semi-arid environment

Abstract The response of wheat crops to elevated CO 2 (eCO 2) was measured and modelled with the Australian Grains Free‐Air CO 2 Enrichment experiment, located at Horsham, Australia. Treatments included CO 2 by water, N and temperature. The location represents a semi‐arid environment with a seasonal VPD of around 0.5 kPa. Over 3 years, the observed mean biomass at anthesis and grain yield ranged from 4200 to 10 200 kg ha−1 and 1600 to 3900 kg ha−1, respectively, over various sowing times and irrigation regimes. The mean observed response to daytime eCO 2 (from 365 to 550 μmol mol−1 CO 2) was relatively consistent for biomass at stem elongation and at anthesis and LAI at anthesis and grain yield with 21%, 23%, 21% and 26%, respectively. Seasonal water use was decreased from 320 to 301 mm (P = 0.10) by eCO 2, increasing water use efficiency for biomass and yield, 36% and 31%, respectively. The performance of six models (APSIM‐Wheat, APSIM‐Nwheat, CAT‐Wheat, CROPSYST, OLEARY‐CONNOR and SALUS) in simulating crop responses to eCO 2 was similar and within or close to the experimental error for accumulated biomass, yield and water use response, despite some variations in early growth and LAI. The primary mechanism of biomass accumulation via radiation use efficiency (RUE) or transpiration efficiency (TE) was not critical to define the overall response to eCO 2. However, under irrigation, the effect of late sowing on response to eCO 2 to biomass accumulation at DC65 was substantial in the observed data (~40%), but the simulated response was smaller, ranging from 17% to 28%. Simulated response from all six models under no water or nitrogen stress showed similar response to eCO 2 under irrigation, but the differences compared to the dryland treatment were small. Further experimental work on the interactive effects of eCO 2, water and temperature is required to resolve these model discrepancies.

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.

Emission factors for estimating fertiliser-induced nitrous oxide emissions from clay soils in Australia’s irrigated cotton industry

As a significant user of nitrogen (N) fertilisers, the Australian cotton industry is a major source of soil-derived nitrous oxide (N2O) emissions. A country-specific (Tier 2) fertiliser-induced emission factor (EF) can be used in national greenhouse gas inventories or in the development of N2O emissions offset methodologies provided the EFs are evidence based. A meta-analysis was performed using eight individual N2O emission studies from Australian cotton studies to estimate EFs. Annual N2O emissions from cotton grown on Vertosols ranged from 0.59kgNha–1 in a 0N control to 1.94kgNha–1 in a treatment receiving 270kgNha–1. Seasonal N2O estimates ranged from 0.51kgNha–1 in a 0N control to 10.64kgNha–1 in response to the addition of 320kgNha–1. A two-component (linear+exponential) statistical model, namely EF (%)=0.29+0.007(e0.037N – 1)/N, capped at 300kgNha–1 describes the N2O emissions from lower N rates better than an exponential model and aligns with an EF of 0.55% using a traditional linear regression model.

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

Nature Plants3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.