Modelling methane emissions and grain yields for a double-rice system in Southern China with DAYCENT and DNDC models.

Methane (CH4) is an important greenhouse gas that contributes to climate change and one of its major sources is rice cultivation. The main aim of this paper was to compare two well-established biogeochemical models, namely Daily Century (DAYCENT) and DeNitrification-DeComposition (DNDC) for estimating CH4 emissions and grain yields for a double-rice cropping system with tillage practice and/or stubble incorporation in the winter fallow season in Southern China. Both models were calibrated and validated using field measured data from November 2008 to November 2014. The calibrated models performed effectively in estimating the daily CH4 emission pattern (correlation coefficient, r = 0.58-0.63, p < 0.001), but model efficiency (EF) values were higher in stubble incorporation treatments, with and without winter tillage (treatments S and WS) (EF = 0.22-0.28) than that in winter tillage without stubble incorporation treatment (W) (EF = -0.06-0.08). We recommend that algorithms for the impacts of tillage practice on CH4 emission should be improved for both models. DAYCENT and DNDC also estimated rice yields for all treatments without a significant bias. Our results showed that tillage practice in the winter fallow season (treatments WS and W) significantly decreased annual CH4 emissions, by 13-37 % (p < 0.05) for measured values, 15-20 % (p < 0.05) for DAYCENT-simulated values, and 12-32 % (p < 0.05) for DNDC-simulated values, respectively, compared to no-till practice (treatments S), but had no significant impact on grain yields.

Changes in soil organic carbon under perennial crops.

This study evaluates the dynamics of soil organic carbon (SOC) under perennial crops across the globe. It quantifies the effect of change from annual to perennial crops and the subsequent temporal changes in SOC stocks during the perennial crop cycle. It also presents an empirical model to estimate changes in the SOC content under crops as a function of time, land use, and site characteristics. We used a harmonized global dataset containing paired-comparison empirical values of SOC and different types of perennial crops (perennial grasses, palms, and woody plants) with different end uses: bioenergy, food, other bio-products, and short rotation coppice. Salient outcomes include: a 20-year period encompassing a change from annual to perennial crops led to an average 20% increase in SOC at 0-30 cm (6.0 ± 4.6 Mg/ha gain) and a total 10% increase over the 0-100 cm soil profile (5.7 ± 10.9 Mg/ha). A change from natural pasture to perennial crop decreased SOC stocks by 1% over 0-30 cm (-2.5 ± 4.2 Mg/ha) and 10% over 0-100 cm (-13.6 ± 8.9 Mg/ha). The effect of a land use change from forest to perennial crops did not show significant impacts, probably due to the limited number of plots; but the data indicated that while a 2% increase in SOC was observed at 0-30 cm (16.81 ± 55.1 Mg/ha), a decrease in 24% was observed at 30-100 cm (-40.1 ± 16.8 Mg/ha). Perennial crops generally accumulate SOC through time, especially woody crops; and temperature was the main driver explaining differences in SOC dynamics, followed by crop age, soil bulk density, clay content, and depth. We present empirical evidence showing that the FAO perennialization strategy is reasonable, underscoring the role of perennial crops as a useful component of climate change mitigation strategies.

Measurement of N2O emissions over the whole year is necessary for estimating reliable emission factors.

Nitrous oxide emission factors (N2O-EF, percentage of N2O-N emissions arising from applied fertilizer N) for cropland emission inventories can vary with agricultural management, soil properties and climate conditions. Establishing a regionally-specific EF usually requires the measurement of a whole year of N2O emissions, whereas most studies measure N2O emissions only during the crop growing season, neglecting emissions during non-growing periods. However, the difference in N2O-EF (ΔEF) estimated using measurements over a whole year (EFwy) and those based on measurement only during the crop-growing season (EFgs) has received little attention. Here, we selected 21 studies including both the whole-year and growing-season N2O emissions under control and fertilizer treatments, to obtain 123 ΔEFs from various agroecosystems globally. Using these data, we conducted a meta-analysis of the ΔEFs by bootstrapping resampling to assess the magnitude of differences in response to management-related and environmental factors. The results revealed that, as expected, the EFwy was significantly greater than the EFgs for most crop types. Vegetables showed the largest ΔEF (0.19%) among all crops (0.07%), followed by paddy rice (0.11%). A higher ΔEF was also identified in areas with rainfall ≥600 mm yr-1, soil with organic carbon ≥1.3% and acidic soils. Moreover, fertilizer type, residue management, irrigation regime and duration of the non-growing season were other crucial factors controlling the magnitude of the ΔEFs. We also found that neglecting emissions from the non-growing season may underestimate the N2O-EF by 30% for paddy fields, almost three times that for non-vegetable upland crops. This study highlights the importance of the inclusion of the non-growing season in the measurements of N2O fluxes, the compilation of national inventories and the design of mitigation strategies.

How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal.

There is growing international interest in better managing soils to increase soil organic carbon (SOC) content to contribute to climate change mitigation, to enhance resilience to climate change and to underpin food security, through initiatives such as international '4p1000' initiative and the FAO's Global assessment of SOC sequestration potential (GSOCseq) programme. Since SOC content of soils cannot be easily measured, a key barrier to implementing programmes to increase SOC at large scale, is the need for credible and reliable measurement/monitoring, reporting and verification (MRV) platforms, both for national reporting and for emissions trading. Without such platforms, investments could be considered risky. In this paper, we review methods and challenges of measuring SOC change directly in soils, before examining some recent novel developments that show promise for quantifying SOC. We describe how repeat soil surveys are used to estimate changes in SOC over time, and how long-term experiments and space-for-time substitution sites can serve as sources of knowledge and can be used to test models, and as potential benchmark sites in global frameworks to estimate SOC change. We briefly consider models that can be used to simulate and project change in SOC and examine the MRV platforms for SOC change already in use in various countries/regions. In the final section, we bring together the various components described in this review, to describe a new vision for a global framework for MRV of SOC change, to support national and international initiatives seeking to effect change in the way we manage our soils.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Systems approaches in global change and biogeochemistry research.

Systems approaches have great potential for application in predictive ecology. In this paper, we present a range of examples, where systems approaches are being developed and applied at a range of scales in the field of global change and biogeochemical cycling. Systems approaches range from Bayesian calibration techniques at plot scale, through data assimilation methods at regional to continental scales, to multi-disciplinary numerical model applications at country to global scales. We provide examples from a range of studies and show how these approaches are being used to address current topics in global change and biogeochemical research, such as the interaction between carbon and nitrogen cycles, terrestrial carbon feedbacks to climate change and the attribution of observed global changes to various drivers of change. We examine how transferable the methods and techniques might be to other areas of ecosystem science and ecology.