The impact of cover crops on soil erosion in the US Midwest.

This study examines the impact of cover crop adoption on soil erosion levels in the United States (US) Midwest. Based on a novel county-level panel data set with information on soil erosion levels and remotely-sensed cover crop acreage, we estimate linear panel fixed effect econometric models and conduct a number of robustness checks to investigate the direct impact of cover crops on two major types of soil erosion (wind and water erosion). Although we find that counties with higher cover crop acreage have statistically lower soil erosion levels due to water, wind, or both, we believe that the magnitudes of the estimated effects are modest. Longer-term multi-year use of cover crops also do not seem to increase the soil erosion reducing effects of cover crops over time. Results from the empirical analysis provide further empirical evidence on the impact of cover crops on soil erosion based on data that captures farmer behavior at the county-level and covers a wider geographical region in the US. Our findings also give insights to policy makers in terms of further understanding the magnitude of the soil erosion benefits from cover crops.

A Decreasing Trend of Nitrous Oxide Emissions From California Cropland From 2000 to 2015.

Mitigation of greenhouse gas emissions from agriculture requires an understanding of spatial-temporal dynamics of nitrous oxide (N2O) emissions. Process-based models can quantify N2O emissions from agricultural soils but have rarely been applied to regions with highly diverse agriculture. In this study, a process-based biogeochemical model, DeNitrification-DeComposition (DNDC), was applied to quantify spatial-temporal dynamics of direct N2O emissions from California cropland employing a wide range of cropping systems. DNDC simulated direct N2O emissions from nitrogen (N) inputs through applications of synthetic fertilizers and crop residues during 2000-2015 by linking the model with a spatial-temporal differentiated database containing data on weather, crop areas, soil properties, and management. Simulated direct N2O emissions ranged from 3,830 to 7,875 tonnes N2O-N yr-1, representing 0.73%-1.21% of the N inputs. N2O emission rates were higher for hay and field crops and lower for orchard and vineyard. State cropland total N2O emissions showed a decreasing trend primarily driven by reductions of cropland area and N inputs, the trend toward growing more orchard, and changes in irrigation. Annual direct N2O emissions declined by 47% from 2000 to 2015. Simulations showed N2O emission variations could be explained not only by cropland area and N fertilizer inputs but also climate, soil properties, and management besides N fertilization. The detailed spatial-temporal emission dynamics and driving factors provide knowledge toward effective N2O mitigation and highlight the importance of coupling process-based models with high-resolution data for characterizing the spatial-temporal variability of N2O emissions in regions with diverse croplands.

Underestimation of N2 O emissions in a comparison of the DayCent, DNDC, and EPIC models.

Process-based models are increasingly used to study agroecosystem interactions and N2 O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N2 O flux from three process-based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. We also evaluated the use of a multi-model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross-model bias to underestimate high magnitude daily and cumulative N2 O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH4+ , and soil NO3- , which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Our results suggest a bias in the model estimation of N2 O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development.

Baseline map of carbon emissions from deforestation in tropical regions.

Policies to reduce emissions from deforestation would benefit from clearly derived, spatially explicit, statistically bounded estimates of carbon emissions. Existing efforts derive carbon impacts of land-use change using broad assumptions, unreliable data, or both. We improve on this approach using satellite observations of gross forest cover loss and a map of forest carbon stocks to estimate gross carbon emissions across tropical regions between 2000 and 2005 as 0.81 petagram of carbon per year, with a 90% prediction interval of 0.57 to 1.22 petagrams of carbon per year. This estimate is 25 to 50% of recently published estimates. By systematically matching areas of forest loss with their carbon stocks before clearing, these results serve as a more accurate benchmark for monitoring global progress on reducing emissions from deforestation.

Benchmark map of forest carbon stocks in tropical regions across three continents.

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a "benchmark" map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ± 6% to ± 53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ± 5% and ca. ± 1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.

Assessing alternatives for mitigating net greenhouse gas emissions and increasing yields from rice production in China over the next twenty years.

Assessments of the efficacy of mitigation of greenhouse gas (GHG) emissions from paddy rice systems have typically been analyzed based on field studies. Extrapolation of the mitigation potential of alternative management practices from field studies to a national scale may be enhanced by spatially explicit process models, like the DeNitrification and DeComposition (DNDC) model. Our objective was to analyze the impacts of mitigation alternatives, management of water, fertilizer, and rice straw, on net GHG emissions (carbon dioxide, methane, and nitrous oxide fluxes), yields, and water use. After constructing a GIS database of soil, climate, rice cropping area and systems, and management practices, we ran DNDC with 21-yr alternative management schemes for each of the approximately 2500 counties in China. Results indicate that, despite large-scale adoption of midseason drainage, there is still large potential for additional methane reductions from Chinese rice paddies of 20 to 60% over 2000-2020. However, changes in management for reducing CH4 emissions simultaneously affect soil carbon dynamics as well as N2O emissions and can thereby reorder the ranking of technical mitigation effectiveness. The order of net GHG emissions reduction effectiveness found here is upland rice > shallow flooding > ammonium sulfate > midseason drainage > off-season straw > slow-release fertilizer > continuous flooding. Most of the management alternatives produced yields comparable to the baseline; however, continuous flooding and upland rice significantly reduced yields. Water management strategies appear to be the most technically promising GHG mitigation alternatives, with shallow flooding providing additional benefits of both water conservation and increased yields.