Segment-level evaluation of the simulated aggregation test: US corn and soybean exploratory experiment

An evaluation of the corn and soybean proportion-estimation accuracy and dot labeling accuracy of the Simulated Aggregation Test, U.S. Corn and Soybean Exploratory Experiment, is presented. These results are in turn compared with the corn and soybean proportion-estimation accuracy and dot labeling accuracy of the Classification Procedures Verification Test

Fiscal year 1981 US corn and soybeans pilot preliminary experiment plan, phase 1

A draft of the preliminary experiment plan for the foreign commodity production forecasting project fiscal year 1981 is presented. This draft plan includes: definition of the phase 1 and 2 U.S. pilot objectives; the proposed experiment design to evaluate crop calendar, area estimation, and area aggregation components for corn and soybean technologies using 1978/1979 crop-year data; a description of individual sensitivity evaluations of the baseline corn and soybean segment classification procedure; and technology and data assessment in support of the corn and soybean estimation technology for use in the U.S. central corn belt

Evaluation of Philippine Corn Statistics

This paper aims to broaden people’s knowledge in which the country’s official corn statistics are produced. It presents the estimation procedures used for area, production and yield. Comparisons are made among the estimators sourced from official and design estimates and from independent sources.economic/development modelling, rice commodities, rice farm, data and statistics, corn and corn products

Evaluation of Philippine Corn Statistics

This paper aims to broaden people’s knowledge in which the country’s official corn statistics are produced. It presents the estimation procedures used for area, production and yield. Comparisons are made among the estimators sourced from official and design estimates and from independent sources.economic/development modelling, rice commodities, rice farm, data and statistics, corn and corn products

ESTIMATION OF U.S. CORN ACREAGE

Crop Production/Industries,

The Effect of Ethanol-Driven Corn Demand on Crop Choice

Since the late 1990s, U.S. production of corn ethanol has risen rapidly. In response to high demand, driven in part by rising ethanol production, corn prices and corn production surged in 2007 when corn plantings reached their highest level since 1944. To increase corn acreage, farmers shifted land to corn from other crops or, possibly, returned uncultivated land (e.g., cropland pasture, CRP land) to corn production. Even before 2007, however, "islands" of relatively high corn prices formed around ethanol plants in the Midwest. Price impacts were usually concentrated around an ethanol plant and ranged between 4.6 cents and 19.6 cents, with an average price increase of 12.5 cents at the plant site. Prices were also affected up to an estimated 68 miles from the plant (McNew and Griffith, 2005). Did these price island effects induce producers to shift to crop rotations that include more corn and/or bring in uncultivated land to corn production? If localized changes did occur in the years before 2007, they may persist into the future even though corn prices have declined absolutely and in relation to prices for soybeans and other crop commodities. The question is important because continuous corn, corn-intensive crop rotations, and shifting pasture or hayland into corn, can adversely affect the environment. This paper develops a discrete choice model that incorporates price island effects, local ethanol capacity, and broader land use change to understand the effect of ethanol-driven price islands on corn acreage, corn rotations, and general land use for cultivated crops. The primary data set in estimating the econometric model is the 2005 Corn Agricultural Resource Management Study (ARMS) survey, collected by the Economic Research Service and the National Agricultural Statistics Service. Because ARMS phase II (field-level) data is geo-referenced, spatially explicit data on corn and soybean prices are linked, along with the proximity of farms to ethanol plants and a soil productivity index. The ARMS Corn data is drawn from the traditional Corn Belt, along with some outlier states including North Carolina and North Dakota. A nested multinomial logit model (NML) is used to estimate producer crop mix response to local corn price islands, land quality, and other farm and location-specific factors. The NML model allows us to account for a range of crop production and land use options which vary in terms of similarity and, therefore, substitutability. At the highest level of the nested model, the farmer decides if he will cultivate his land or leave it uncultivated. If he chooses to cultivate his land, he needs to decide what crop to plant, for example, a corn-soybean rotation, wheat, or some “other” crop. The farmer will choose the crop and rotation pattern that will provide him with the greatest return, given prices and inputs. In this paper, the NML estimates the probability that a farmer will choose corn and soybeans (conditional on the choice of corn or soybeans) at the lower level and the choice among corn or soybeans, wheat, and “other” crops at the upper level, where the probability is a function of a corn/soybean price ratio; ethanol capacity index; livestock indicator variable; irrigation; soil quality and protected land statuses (highly erodible land); and household and farm characteristics. Because parameters cannot be directly interpreted in this model, the marginal effects and elasticities are examined. The authors find that in both levels of estimation, soil productivity and livestock value influence a farmer’s decision to plant corn or soybeans, wheat or some other crop. The estimation of our lower level confirms that local prices have a strong influence on whether a farmer will choose to plant corn or soybeans, while our upper level estimation may suggest that an increase in local ethanol capacity will encourage farmers to plant corn or soybean relative to both wheat and “other”. Information on the influence of "price islands" on farm behavior, including farm crop and rotation patterns and individual farmer land use decisions, could have environmental and other implications. This work could be extended by linking land use change to nutrient runoff and loads in water, possible soil erosion, and other environmental impacts from continuous corn rotations.Crop Production/Industries,

Farm Level Impacts of Bt Corn Adoption in a Developing Country: Evidence from the Philippines

This article examines the ex post farm-level impacts of Bt corn adoption in the Philippines. Using an econometric approach that addresses simultaneity, selection, and censoring problems, we show that Bt corn adoption provides modest but statistically significant increases in farm-level yields and profits. Furthermore, our results suggest that farm-level yield and profit impacts of Bt corn adoption are underestimated when censoring in the pesticide application variable is not considered in the estimation procedures. Previous literature have emphasized the importance of simultaneity and selection problems but this is the first study that have raised the issue of censoring problems in estimating the farm-level effects of Bt corn adoption.Bt, censoring, corn, farm level impacts genetically modified crops, pesticide use, technology adoption, Crop Production/Industries, Q12, Q16,

Impact Assessment of Bt Corn Adoption in the Philippines

This article examines the impact of Bt corn adoption in the Philippines using an econometric approach that addresses simultaneity, selection, and censoring problems. Although previous literature emphasizes the importance of simultaneity and selection problems, this is the first study that addresses the issue of censoring in estimating the effects of Bt corn adoption at the farm in a developing country context. We show that Bt corn adoption provides modest but statistically significant increases in farm yields and profits. Furthermore, our results provide some evidence of inference errors that can potentially arise when censoring in the pesticide application variable is ignored in the estimation procedures.Bt, censoring, corn, farm level impacts, genetically modified crops, pesticide use, technology adoption, International Development, Production Economics, Q12, Q16,

AgRISTARS: Foreign commodity production forecasting. The 1980 US corn and soybeans exploratory experiment

The U.S. corn and soybeans exploratory experiment is described which consisted of evaluations of two technology components of a production forecasting system: classification procedures (crop labeling and proportion estimation at the level of a sampling unit) and sampling and aggregation procedures. The results from the labeling evaluations indicate that the corn and soybeans labeling procedure works very well in the U.S. corn belt with full season (after tasseling) LANDSAT data. The procedure should be readily adaptable to corn and soybeans labeling required for subsequent exploratory experiments or pilot tests. The machine classification procedures evaluated in this experiment were not effective in improving the proportion estimates. The corn proportions produced by the machine procedures had a large bias when the bias correction was not performed. This bias was caused by the manner in which the machine procedures handled spectrally impure pixels. The simulation test indicated that the weighted aggregation procedure performed quite well. Although further work can be done to improve both the simulation tests and the aggregation procedure, the results of this test show that the procedure should serve as a useful baseline procedure in future exploratory experiments and pilot tests

Generalized Hedge Ratio Estimation with an Unknown Model

Myers and Thompson (1989) noted that the model specification could have a large impact on the hedge ratio estimated. A huge literature exists on estimating hedge ratios, but the literature is lacking a formal treatment of model specification uncertainty. This research accomplishes that task by taking a Bayesian approach to hedge ratio estimation, where specification uncertainty is explicitly modeled. The methodology is applied to data on hedging of corn and soybeans and on cross-hedging of corn oil using soybean oil futures. Results show the potential benefits and insights gained from such an approach.Marketing,