A hierarchical Bayesian model for forecasting state-level corn yield

Historically, the National Agricultural Statistics Service crop forecasts and estimates have been determined by a group of commodity experts called the Agricultural Statistics Board (ASB). The corn yield forecasts for the “speculative region,” ten states that account for approximately 85 % of corn production, are based on two sets of monthly surveys, a farmer interview survey and a field measurement survey. The members of the ASB subjectively determine a forecast on the basis of a discussion of the survey data and auxiliary information about weather, average planting dates, and crop maturity. The ASB uses an iterative procedure, where initial state estimates are adjusted so that the weighted sum of the final state estimates is equal to a previously-determined estimate for the speculative region. Deficiencies of the highly subjective ASB process are lack of reproducibility and a measure of uncertainty. This paper describes the use of Bayesian methods to model the ASB process in a way that leads to objective forecasts and estimates of the corn yield. First, we use small area estimation techniques to obtain state-level forecasts. Second, we describe a way to adjust the state forecasts so that the weighted sum of the state forecasts is equal to a previously-determined regional forecast. We use several diagnostic techniques to assess the goodness of fit of various models and their competitors. We use Markov chain Monte Carlo methods to fit the models to both historic and current data from the two monthly surveys. Our results show that our methodology can provide reasonable and objective forecasts of corn yields for states in the speculative region.     

Assessment of toxicity of two types of drill cuttings from a drilling rig on the Trinidad East coast using Metamysidopsis insularis

This study investigated the relative toxicity of water-based cuttings (WBC) and synthetic oil-based cuttings (SOBC) to the marine species, Metamysidopsis insularis. Results obtained indicate that SOBC (LC₅₀ 1.2 (0.85–1.6)%) was more toxic to M. insularis than WBC (LC₅₀ 9.9 (8.3–11.8)%), with similar metal contents in both types of cuttings. The elevated levels of metals found in the cuttings when compared to surficial sediments may be due to both drilling fluids, as well as the rock strata from which the cuttings were obtained. Furthermore, total petroleum hydrocarbon (TPH) analyses demonstrated significantly higher concentrations of TPH present in SOBC (14,680?±?1250?mg?kg^?1) compared to WBC (860?±?115?mg?kg^?1). This may also be due to the increased depth and hence oil bearing rock formations in the selected sampling area, along with the associated synthetic oil-based drilling fluid. These findings therefore supply evidence that drill cuttings after treatment prior to discharge are potentially toxic to marine organisms.     

Quantitative assessment of the toxic effects of heavy metals on 1,2-dichloroethane biodegradation in co-contaminated soil under aerobic condition

1,2-Dichloroethane (1,2-DCA) is one of the most hazardous pollutant of soil and groundwater, and is produced in excess of 5.44 × 10⁹ kg annually. Owing to their toxicity, persistence and potential for bioaccumulation, there is a growing interest in technologies for their removal. Heavy metals are known to be toxic to soil microorganisms at high concentrations and can hinder the biodegradation of organic contaminants. In this study, the inhibitory effect of heavy metals, namely; arsenic, cadmium, mercury and lead, on the aerobic biodegradation of 1,2-DCA by autochthonous microorganisms was evaluated in soil microcosm setting. The presence of heavy metals was observed to have a negative impact on the biodegradation of 1,2-DCA in both soil samples tested, with the toxic effect being more pronounced in loam soil, than in clay soil. Generally, 75 ppm As³⁺, 840 ppm Hg²⁺, and 420 ppm Pb²⁺ resulted in 34.24%, 40.64%, and 45.94% increase in the half live (t_½) of 1,2-DCA, respectively, in loam soil, while concentrations above 127.5 ppm Cd²⁺, 840 ppm Hg²⁺ and 420 ppm of Pb²⁺ and less than 75 ppm As³⁺ was required to cause a >10% increase in the t_½ of 1,2-DCA in clay soil. A dose-dependent relationship between degradation rate constant (k₁) of 1,2-DCA and metal ion concentrations was observed for all the heavy metals tested, except for Hg²⁺. This study demonstrated that different heavy metals have different impacts on the degree of 1,2-DCA degradation. Results also suggest that the degree of inhibition is metal specific and is also dependent on several factors including; soil type, pH, moisture content and available nutrients.     

Impacts of heavy metals on 1,2-dichloroethane biodegradation in co-contaminated soil

1,2-Dichloroethane (DCA), a potential mutagen and carcinogen, is commonly introduced into the environment through its industrial and agricultural use. In this study, the impact of lead and mercury on DCA degradation in soil was investigated, owing to the complex co-contamination problem frequently encountered in most sites. 1,2-Dichloroethane was degraded readily in both contaminated loam and clay soils with the degradation rate constants ranging between 0.370-0.536 week1 and 0.309-0.417 week1, respectively...     

Benchmarking a triplet of official estimates

The publication of official statistics at different levels of aggregation requires a benchmarking step. Difficulties arise when a benchmarking method needs to be applied to a triplet of related estimates, at multiple stages of aggregation. For ratios of totals, external benchmarking constraints for the triplet (numerator, denominator, ratio) are that the weighted sum of denominator/numerator/ratio estimates equals to a constant. The benchmarking weight, applied to the ratio estimates, is a function of the denominator estimates. For example, the United States Department of Agriculture’s National Agricultural Statistics Service’s county-level, end-of-season acreage, production and yield estimates need to aggregate to the corresponding agricultural statistics district-level estimates, which also need to aggregate to the corresponding prepublished state-level values. Moreover, the definition of yield, as the ratio of production to harvested acreage, needs to hold at the county level, the agricultural statistics district level and the state level. We discuss different methods of applying benchmarking constraints to a triplet (numerator, denominator, ratio), at multiple stages of aggregation, where the denominator and the ratio are modeled and the numerator is derived. County-level and agricultural statistics district-level, end-of-season acreage, production and yield estimates are constructed and compared using the different methods. Results are illustrated for 2014 corn and soybean in Indiana, Iowa and Illinois.