PAYOFFS TO FARM MANAGEMENT: HOW IMPORTANT IS CROP MARKETING?

In production agriculture, good management is demonstrated by profits that are persistenly greater than those of similar neighboring farms. This research examined the effects of management practices on risk-adjusted profit per acre for Kansas farms over 1990-1999. The management practices were price, cost, yield, planting intensity, and technology adoption (less-tillage). Cost management, planting intensity, and technology adoption had the greatest effect on profit per acre, and cash price management was found to have the smallest impact. If producers wish to have continuously high profits, their efforts are best spent in management practices over which they have the most control.farm management, marketing, risk, technology adoption, Farm Management, Marketing,

USING SATELLITE IMAGERY IN KANSAS CROP YIELD AND NET FARM INCOME FORECASTS

Remotely sensed data have been used in the past to predict crop yields. This research attempts to incorporate remotely sensed data into a net farm income projection model. Using in-sample regressions, satellite imagery appears to increase prediction accuracy in the time periods prior to USDA's first crop production estimate for wheat and corn. Remotely sensed data improved model performance more in the western regions of the state than in the eastern regions. However, in a jackknife out-of-sample framework, the satellite imagery appeared to statistically improve only 8 of the 81 models (9 crop reporting districts by 9 forecasting horizons) estimated. Moreover, 41 of the 81 models were statistically better without the satellite imagery data. This indicates that perhaps the functional form of net farm income has not been well-specified since additional information should generally not cause a model to deteriorate.net farm income, remote sensing, satellite imagery, Crop Production/Industries,

USING SATELLITE IMAGERY IN PREDICTING KANSAS FARMLAND VALUES

Can remotely sensed imagery improve hedonic land price models? A remotely sensed variable was added to a hedonic farmland value model as a proxy for land productivity. Land cover data were used to obtain urban and recreational effects as well. The urban and recreational effects were statistically significant but economically small. The remotely sensed productivity variable was statistically significant and economically large, indicating that knowing the "greenness" of the land increased the explanatory power of the hedonic price model. Thus, depending upon the cost of this information, including remotely sensed imagery in traditional hedonic land price models is economically beneficial.Land Economics/Use,

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

USING SATELLITE IMAGERY IN KANSAS CROP YIELD AND NET FARM INCOME FORECASTS

Remotely sensed data have been used in the past to predict crop yields. This research attempts to incorporate remotely sensed data into a net farm income projection model. Using in-sample regressions, satellite imagery appears to increase prediction accuracy in the time periods prior to USDA's first crop production estimate for wheat and corn. Remotely sensed data improved model performance more in the western regions of the state than in the eastern regions. However, in a jackknife out-of-sample framework, the satellite imagery appeared to statistically improve only 8 of the 81 models (9 crop reporting districts by 9 forecasting horizons) estimated. Moreover, 41 of the 81 models were statistically better without the satellite imagery data. This indicates that perhaps the functional form of net farm income has not been well-specified since additional information should generally not cause a model to deteriorate

PAYOFFS TO FARM MANAGEMENT: HOW IMPORTANT IS CROP MARKETING?

In production agriculture, good management is demonstrated by profits that are persistenly greater than those of similar neighboring farms. This research examined the effects of management practices on risk-adjusted profit per acre for Kansas farms over 1990-1999. The management practices were price, cost, yield, planting intensity, and technology adoption (less-tillage). Cost management, planting intensity, and technology adoption had the greatest effect on profit per acre, and cash price management was found to have the smallest impact. If producers wish to have continuously high profits, their efforts are best spent in management practices over which they have the most control

USING SATELLITE IMAGERY IN PREDICTING KANSAS FARMLAND VALUES

Can remotely sensed imagery improve hedonic land price models? A remotely sensed variable was added to a hedonic farmland value model as a proxy for land productivity. Land cover data were used to obtain urban and recreational effects as well. The urban and recreational effects were statistically significant but economically small. The remotely sensed productivity variable was statistically significant and economically large, indicating that knowing the "greenness" of the land increased the explanatory power of the hedonic price model. Thus, depending upon the cost of this information, including remotely sensed imagery in traditional hedonic land price models is economically beneficial