Readdressing the Fertilizer Problem

The production literature has shown that inputs such as fertilizer can be defined as risk-increasing. However, farmers also consistently overapply nitrogen. A model of optimal input use under uncertainty is used to address this paradox. Using experimental data, a stochastic production relationship between yield and soil nitrate is estimated. Numerical results show that input uncertainty may cause farmers to overapply nitrogen. Survey data suggest that farmers are risk averse, but prefer small chances of high yields compared to small chances of crop failures when expected yields are equivalent. Furthermore, yield risk and yield variability are not equivalent.corn, nitrogen fertilizer, risk-increasing, yield risk, Crop Production/Industries,

Greenhouse Gas Impacts of Ethanol from Iowa Corn: Life Cycle Analysis versus System-wide Accounting

Life cycle analysis (LCA) is the standard approach used to evaluate the greenhouse gas (GHG) benefits of biofuels. However, it is increasingly recognized that LCA results do not account for some impacts including land use changes that have important implications on GHGs. Thus, an alternative accounting system that goes beyond LCA is needed. In this paper, we contribute to the literature by laying out the basics of a system-wide accounting (SWA) method that takes into account all potential changes in GHGs resulting from biofuel expansion. We applied both LCA and SWA to assess the GHG impacts of ethanol based on Iowa corn. Growing corn in rotation with soybeans generated 35% less GHG emissions than growing corn after corn. Based on average corn production, ethanol's GHG benefits were lower in 2007 than in 2006 because of an increase in continuous corn in 2007. When only additional corn was considered, ethanol emitted about 22% less GHGs than gasoline. Results from SWA varied with the choice of baseline and the definition of geographical boundaries. Using 2006 as a baseline and 2007 as a scenario, corn ethanol's benefits were about 20% of the emissions of gasoline. If we expand geographical limits beyond Iowa, but assume the same emission rates for soybean production and land use changes as those in Iowa, then corn ethanol generated more GHG emissions than gasoline. These results highlight the importance of boundary definition for both LCA and SWAbiofuels, corn ethanol, greenhouse gas, life cycle analysis, system-wide accounting, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy,

THE ENVIRONMENTAL EFFECTS OF FREEDOM TO FARM

The Federal Agriculture Improvement and Reform Act (FAIR) of 1996 ended commodity specific subsidies and resulted in a significant shift in corn and soybean production in 1997. While conservation compliance improved the environmental health of the Central U.S., changes in production due to the FAIR act have tempered these improvements.Agricultural and Food Policy, Environmental Economics and Policy,

Greenhouse gas impacts of ethanol from Iowa corn: Life cycle assessment versus system wide approach

Life cycle assessment (LCA) is the standard approach used to evaluate the greenhouse gas (GHG) benefits of biofuels. However, the need for the appropriate use of LCA in policy contexts is highlighted by recent findings that corn-based ethanol may actually increase GHG emissions. This is in contrary to most existing LCA results. LCA estimates can vary across studies due to heterogeneities in inputs and production technology. Whether marginal or average impacts are considered can matter as well. Most important of all, LCA is product-centered. The determination of the impact of biofuels expansion requires a system wide approach (SWA) that accounts for impacts on all affected products and processes. This paper presents both LCA and SWA for ethanol based on Iowa corn. LCA was conducted in several different ways. Growing corn in rotation with soybean generates 35% less GHG emissions than growing corn after corn. Based on average corn production, ethanol\u27s GHG benefits were lower in 2007 than in 2006 because of an increase in continuous corn in 2007. When only additional corn was considered, ethanol emitted about 22% less GHGs than gasoline. SWA was applied to two simple cases. Using 2006 as a baseline and 2007 as a scenario, corn ethanol\u27s benefits were about 20% of the emissions of gasoline. If geographical limits are expanded beyond Iowa, then corn ethanol could generate more GHG emissions than gasoline. These results highlight the importance of boundary definition for both LCA and SWA

RELATIVE EFFICIENCY OF SEQUESTERING CARBON IN AGRICULTURAL SOILS THROUGH SECOND BEST MARKET-BASED INSTRUMENTS

The total expected cost of sequestering carbon in agricultural soils is estimated under a possible EQIP program offering a per-acre subsidy to adopt conservation tillage and a carbon credit program where producers can sell their carbon credit in an external market. Both programs are compared to the minimum cost solution.Land Economics/Use, Resource /Energy Economics and Policy,

Comparison of the EyeSys Corneal Analysis System and peripheral keratometry using a B&L keratometer and a lighted fixation device

The purpose of this study was to develop a keratometric device that would give a peripheral corneal measurement approximately 3.0mm from the center of the cornea and to compare the accuracy of the peripheral keratometry readings to an industry standard, a computerized corneal topographer. The EyeSys Corneal Analysis System by EyeSys laboratories in Houston, Texas, was chosen as a representative of available computerized corneal topographers. A fixation device, with peripheral fixation targets was created and attached to a standard Bausch & Lomb keratometer. Thirty-one subjects (five in the initial phase and twenty-six in the final phase) who were free from corneal disease and were not contact lens wearers, were subjects for this study. Each subject had four keratometric readings per eye taken 3.0mm from the center of the cornea and compared to the same location on their topographic map. Ninety-one percent of all readings fell within ±0.500. The device may prove to be a useful tool to aid in the base curve selection when fitting RGP lenses on both normal and pathologic eyes (i.e. keratoconus, post-keratoplasty and post-refractive surgery)

Subfield profitability analysis reveals an economic case for cropland diversification

Public agencies and private enterprises increasingly desire to achieve ecosystem service outcomes in agricultural systems, but are limited by perceived conflicts between economic and ecosystem service goals and a lack of tools enabling effective operational management. Here we use Iowa—an agriculturally homogeneous state representative of the Maize Belt—to demonstrate an economic rationale for cropland diversification at the subfield scale. We used a novel computational framework that integrates disparate but publicly available data to map ∼3.3 million unique potential management polygons (9.3 Mha) and reveal subfield opportunities to increase overall field profitability. We analyzed subfield profitability for maize/soybean fields during 2010–2013—four of the most profitable years in recent history—and projected results for 2015. While cropland operating at a loss of US$ 250 ha−1 or more was negligible between 2010 and 2013 at 18 000–190 000 ha (\u3c2% of row-crop land), the extent of highly unprofitable land increased to 2.5 Mha, or 27% of row-crop land, in the 2015 projection. Aggregation of these areas to the township level revealed ‘hotspots’ for potential management change in Western, Central, and Northeast Iowa. In these least profitable areas, incorporating conservation management that breaks even (e.g., planting low-input perennials), into low-yielding portions of fields could increase overall cropland profitability by 80%. This approach is applicable to the broader region and differs substantially from the status quo of ‘top-down’ land management for conservation by harnessing private interest to align profitability with the production of ecosystem services

Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables

Recent advancements in remote sensing technology, specifically Light Detection and Ranging (LiDAR) sensors, provide the data needed to quantify forest characteristics at a fine spatial resolution over large geographic domains. From an inferential standpoint, there is interest in prediction and interpolation of the often sparsely sampled and spatially misaligned LiDAR signals and forest variables. We propose a fully process-based Bayesian hierarchical model for above ground biomass (AGB) and LiDAR signals. The processbased framework offers richness in inferential capabilities, e.g., inference on the entire underlying processes instead of estimates only at pre-specified points. Key challenges we obviate include misalignment between the AGB observations and LiDAR signals and the high-dimensionality in the model emerging from LiDAR signals in conjunction with the large number of spatial locations. We offer simulation experiments to evaluate our proposed models and also apply them to a challenging dataset comprising LiDAR and spatially coinciding forest inventory variables collected on the Penobscot Experimental Forest (PEF), Maine. Our key substantive contributions include AGB data products with associated measures of uncertainty for the PEF and, more broadly, a methodology that should find use in a variety of current and upcoming forest variable mapping efforts using sparsely sampled remotely sensed high-dimensional data