Effects of No-Tillage Production Practices on Crop Yields as Influenced by Crop and Growing Environment Factors

This paper evaluated differences between yields of no-tillage compared to conventional or reduced tillage and their associated downside risk. Six crops were evaluated along with how those yields and risks differed by various environmental factors such geographic location, precipitation, soil type and how long the practice had been used.no-tillage, conservation, conventional tillage, downside-risk, yield, Agribusiness, Environmental Economics and Policy, Farm Management, Land Economics/Use, Production Economics, Risk and Uncertainty,

Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region

tQuantifying spatial and temporal patterns of carbon sources and sinks and their uncertainties acrossagriculture-dominated areas remains challenging for understanding regional carbon cycles. Character-istics of local land cover inputs could impact the regional carbon estimates but the effect has not beenfully evaluated in the past. Within the North American Carbon Program Mid-Continent Intensive (MCI)Campaign, three models were developed to estimate carbon fluxes on croplands: an inventory-basedmodel, the Environmental Policy Integrated Climate (EPIC) model, and the General Ensemble biogeo-chemical Modeling System (GEMS) model. They all provided estimates of three major carbon fluxes oncropland: net primary production (NPP), net ecosystem production (NEP), and soil organic carbon (SOC)change. Using data mining and spatial statistics, we studied the spatial distribution of the carbon fluxesuncertainties and the relationships between the uncertainties and the land cover characteristics. Resultsindicated that uncertainties for all three carbon fluxes were not randomly distributed, but instead formedmultiple clusters within the MCI region. We investigated the impacts of three land cover characteristicson the fluxes uncertainties: cropland percentage, cropland richness and cropland diversity. The resultsindicated that cropland percentage significantly influenced the uncertainties of NPP and NEP, but noton the uncertainties of SOC change. Greater uncertainties of NPP and NEP were found in counties withsmall cropland percentage than the counties with large cropland percentage. Cropland species richnessand diversity also showed negative correlations with the model uncertainties. Our study demonstratedthat the land cover characteristics contributed to the uncertainties of regional carbon fluxes estimates.The approaches we used in this study can be applied to other ecosystem models to identify the areaswith high uncertainties and where models can be improved to reduce overall uncertainties for regionalcarbon flux estimates

Definition, Capabilities, and Components of a Terrestrial Carbon Monitoring System

Research efforts for effectively and consistently monitoring terrestrial carbon are increasing in number. As such, there is a need to define carbon monitoring and how it relates to carbon cycle science and carbon management. There is also a need to identify capabilities of a carbon monitoring system and the system components needed to develop the capabilities. Capabilities that enable the effective application of a carbon monitoring system for monitoring and management purposes may include: reconciling carbon stocks and fluxes, developing consistency across spatial and temporal scales, tracking horizontal movement of carbon, attribution of emissions to originating sources, cross-sectoral accounting, uncertainty quantification, redundancy and policy relevance. Focused research is needed to integrate these capabilities for sustained estimates of carbon stocks and fluxes. Additionally, if monitoring is intended to inform management decisions, management priorities should be considered prior to development of a monitoring system

Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data

Accurate quantification and clear understanding of regional scale cropland carbon (C) cycling is critical for designing effective policies and management practices that can contribute toward stabilizing atmospheric CO2 concentrations. However, extrapolating site-scale observations to regional scales represents a major challenge confronting the agricultural modeling community. This study introduces a novel geospatial agricultural modeling system (GAMS) exploring the integration of the mechanistic Environmental Policy Integrated Climate model, spatially-resolved data, surveyed management data, and supercomputing functions for cropland C budgets estimates. This modeling system creates spatiallyexplicit modeling units at a spatial resolution consistent with remotely-sensed crop identification and assigns cropping systems to each of them by geo-referencing surveyed crop management information at the county or state level. A parallel computing algorithm was also developed to facilitate the computationally intensive model runs and output post-processing and visualization. We evaluated GAMS against National Agricultural Statistics Service (NASS) reported crop yields and inventory estimated county-scale cropland C budgets averaged over 2000e2008. We observed good overall agreement, with spatial correlation of 0.89, 0.90, 0.41, and 0.87, for crop yields, Net Primary Production (NPP), Soil Organic C (SOC) change, and Net Ecosystem Exchange (NEE), respectively. However, we also detected notable differences in the magnitude of NPP and NEE, as well as in the spatial pattern of SOC change. By performing crop-specific annual comparisons, we discuss possible explanations for the discrepancies between GAMS and the inventory method, such as data requirements, representation of agroecosystem processes, completeness and accuracy of crop management data, and accuracy of crop area representation. Based on these analyses, we further discuss strategies to improve GAMS by updating input data and by designing more efficient parallel computing capability to quantitatively assess errors associated with the simulation of C budget components. The modularized design of the GAMS makes it flexible to be updated and adapted for different agricultural models so long as they require similar input data, and to be linked with socio-economic models to understand the effectiveness and implications of diverse C management practices and policies

An approach for verifying biogenic greenhouse gas emissions inventories with atmospheric CO₂ concentration data

Verifying national greenhouse gas (GHG) emissions inventories is a critical step to ensure that reported emissions data to the United Nations Framework Convention on Climate Change (UNFCCC) are accurate and representative of a country\u27s contribution to GHG concentrations in the atmosphere. Furthermore, verifying biogenic fluxes provides a check on estimated emissions associated with managing lands for carbon sequestration and other activities, which often have large uncertainties. We report here on the challenges and results associated with a case study using atmospheric measurements of CO₂ concentrations and inverse modeling to verify nationally-reported biogenic CO₂ emissions. The biogenic CO₂ emissions inventory was compiled for the Mid-Continent region of United States based on methods and data used by the US government for reporting to the UNFCCC, along with additional sources and sinks to produce a full carbon balance. The biogenic emissions inventory produced an estimated flux of −408 ± 136 Tg CO₂ for the entire study region, which was not statistically different from the biogenic flux of −478 ± 146 Tg CO₂ that was estimated using the atmospheric CO₂concentration data. At sub-regional scales, the spatial density of atmospheric observations did not appear sufficient to verify emissions in general. However, a difference between the inventory and inversion results was found in one isolated area of West-central Wisconsin. This part of the region is dominated by forestlands, suggesting that further investigation may be warranted into the forest C stock or harvested wood product data from this portion of the study area. The results suggest that observations of atmospheric CO₂ concentration data and inverse modeling could be used to verify biogenic emissions, and provide more confidence in biogenic GHG emissions reporting to the UNFCCC

North American carbon dioxide sources and sinks: magnitude, attribution, and uncertainty

North America is both a source and sink of atmospheric carbon dioxide (CO2). Continental sources - such as fossil-fuel combustion in the US and deforestation in Mexico - and sinks - including most ecosystems, and particularly secondary forests - add and remove CO2 from the atmosphere, respectively. Photosynthesis converts CO2 into carbon as biomass, which is stored in vegetation, soils, and wood products. However, ecosystem sinks compensate for only similar to 35% of the continent's fossil-fuel-based CO2 emissions; North America therefore represents a net CO2 source. Estimating the magnitude of ecosystem sinks, even though the calculation is confounded by uncertainty as a result of individual inventory- and model-based alternatives, has improved through the use of a combined approach. Front Ecol Environ 2012; 10(10): 512-519, doi:10.1890/12006

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead