Agricultural Profits and Farm Household Wealth: A Farm-level and Cross-sectional Analysis

This study examines the relationship between agricultural profits and farm household wealth across locations and farm sizes in U.S. agriculture. Farmland has out-performed non-farm investments over the past decade. Thus, households may want to keep their farmland to build wealth, even if it requires them to earn off-farm income.Land Economics/Use,

Agricultural Profits and Farm Household Wealth: A Farm-level Analysis Using Repeated Cross Sections

This study examines the relationship between agricultural profits and farm household wealth across locations and farm sizes in U.S. agriculture. A multiperiod household model is used to develop hypotheses for testing. Results indicate that farmland has out-performed nonfarm investments over the past decade. Thus, households may want to keep their farmland to build wealth, even if it requires them to earn off-farm income. The analysis implies that decision will be made based on farm household wealth factors having little to do with agriculture.farm household, off-farm income, production profits, wealth, Agribusiness, Demand and Price Analysis, Farm Management, Risk and Uncertainty, Q12, Q14,

Off-farm Income and Investments in Farm Assets: A Double Hurdle Approach

The farm household structure is a complex set of inter-relationships between and among a variety of internal and external factors involving consumption, investment, and income-earning activities. In this paper we use ARMS data to explore the contribution of off-farm income to the viability of the farm business. We focus on the link between off-farm income and farm investment and whether off-farm income drives on-farm investment. The results indicate the importance of farm characteristics such as type, size, and location on the probability of investment but lead us to reject the hypothesis that off farm income is driving farm investment. Further research will be needed to further unweave some of the complex relationships involved in the farm household structure. Keywords: Farm investments, off-farm income, double hurdleOff-farm income, farm investment, double hurdle, Agricultural Finance, Financial Economics, D1, J2, Q12,

Determinants of Farm Household Income Diversification in the United States: Evidence from Farm-Level Data

This study examines the determinants of income diversification of farm households in the United States. Farm households allocate their time between farm and off-farm activities to help stabilized household income (consumption). What characterizes those households who engage in off-farm activities? Is there any pattern over time? Using 1999, 2003 and 2007 farm-level data from the USDA’s Agricultural Resource Management Survey (ARMS), this study estimates intensity of off-farm income (or income diversification). The results show that older operators, full owners, and small farms have higher intensity of off-farm income in total household income. In contrast, dairy farms, vertically coordinated farms and farms located in the Southern and Pacific regions have lower intensity of off-farm income. In other words, household incomes of these farms are less likely to be diversified.Tobit, income diversification, vertical integration, tenure, farm households, Agricultural Finance, Consumer/Household Economics, D1, J2, Q12,

Off-farm Income and Risky Investments: What Happens to Farm and Nonfarm Assets?

Off-farm work improves and reduces the riskiness of household income. Theoretical analyses reveal that the level and riskiness of off-farm income affect demand for farm/nonfarm investments. A two-limit Tobit model is estimated using ARMS data for 1996-2003. The impact on investment behaviour is evaluated.Farm Management,

Flow cytometric measurement of glutathione content of human cancer biopsies.

Rice et al. (1986) have described a flow cytometric method where the non-fluorescent probe monochlorobimane (mBCl) forms a fluorescent adduct with cellular glutathione (GSH) under the action of glutathione-S-transferase. We show here that for EMT6 carcinosarcoma cells there is a close correlation between mean cell fluorescence, expressed as a ratio to that of fluorescence calibration beads, and biochemically determined GSH content over the range 0.2-2.0 fmol cell-1. Single cell suspensions from 14 human cancers were prepared by 23-gauge needle aspiration or mechanical disaggregation of surgical specimens, stained using mBCl and examined by flow cytometry. There was a wide range in individual cell fluorescence, which in contrast to EMT6 cells was not strongly correlated with Coulter volume. By comparing tumour cell fluorescence to that of calibration beads, and assuming that the relationship with GSH content for EMT6 holds for other cells, a mean GSH content of 0.95 fmol cell-1 was derived for nine carcinomas, and 0.21 fmol cell-1 for five non-Hodgkin's lymphomas. Although this semi-quantitation needs further validation, the method used here is rapid, gives an indication of heterogeneity of tumour cell GSH content, and can be applied to fine needle biopsy samples. It therefore shows promise as a means for studying prospectively the relationship of GSH content to clinical drug and radiation sensitivity, and for monitoring the effects of agents such as buthionine sulphoximine which are intended to improve treatment results through tumour cell GSH depletion

Rating the Financial Health of U.S. Production Agriculture using Synthetic Credit Rating

Agribusiness, Agricultural and Food Policy, Production Economics,