In response to the public's concerns about animal welfare in swine husbandry, the pig (Sus scrofa domesticus) sector introduced improved measures to focus on single rather than multiple dimensions of animal welfare concerns without accounting for their impact on public attitudes. These measures failed to improve attitudes to pig husbandry. The present study uses a more comprehensive approach by evaluating animal welfare measures in terms of their effect on animal welfare, farm income and public attitudes. Four measures were defined for each of the following societal aspects of sow husbandry: piglet mortality; tail biting and the indoor housing of gestating sows. A simulation model was developed to estimate the effects of the measures and Data Envelopment Analysis used to compare measures in terms of their effects on animal welfare, farm income and public attitudes. Only piglet mortality measures were found to have a positive effect on farm income but they showed a relatively low effect on animal welfare and public attitudes. The most efficient measure was that which included straw provision, daylight and increased group sizes for gestating sows. The level of improvement of a measure on animal welfare did not necessarily equate to the same level of improvement in public attitudes or decrease in farm income.

We examine the impact of the rapidly expanding mobile banking service “mobile money” on rural households’ decision to adopt modern agricultural inputs and its resultant effect on agricultural income using plot, household, and community-level panel data from rural Uganda. The main findings indicate that mobile money adoption increases per capita farm income by 13%. Pathway analyses show that mobile money adoption increases the likelihood of using chemical fertilizer on maize plots by 11 percentage points. Mobile money adoption increases the likelihood of high-yielding maize seeds adoption on maize plots by 8.2 percentage points. In the Ugandan context of rapid decline in soil fertility and very low adoption of fertilizer and modern seeds, mobile money provides an avenue to finance agricultural intensification.

This article explores whether income underreporting for tax purposes can explain why the majority of U.S. farmers earn low or negative net farm income. Using 10 years of U.S. Department of Agriculture farm-level data, the extent of underreporting is estimated by exploiting the fact that farm households face an incentive to underreport farm income that varies with their reported off-farm income. Results indicate that 39% of total farm income is underreported. For large farms, the results imply a substantial discrepancy between reported and earned farm income. For small-scale operations, underreporting reduces but does not eliminate the gap between farm and off-farm wages.

A linear programming model was used to determine which crop rotations and weed management systems result in the highest net farm income for each of three farm sizes (120, 240, and 480 hectares) under alternative tillage systems. Test plot data for the years 1981 through 1988 from the Purdue University Agronomy Farm, which has highly productive, well-drained soils, were analyzed. Net incomes for no-till tillage systems on all farms in the model were consistently and significantly lower than incomes for moldboard and chisel plow tillage systems due to slightly lower yields and substantially higher herbicide costs. Generally, net farm incomes were slightly higher with a moldboard plow versus chisel plow tillage system. Also, as farm size increased, per hectare net incomes increased. About 80% of the time under moldboard or chisel plow tillage systems, the model chose as optimal the lowest of three herbicide application rates. A corn/soybean rotation was chosen as optimal on 56% of the farm area analyzed, versus 25% for continuous corn and 13% for a corn/soybean/wheat rotation.

The short-run economic consequences of bans on herbicides commonly applied to corn (Zea mays L.) and soybeans [Glycine max (L.) Merr.] on a typical Indiana farm were analyzed. A dinitroaniline ban reduced soybean yields and net farm income 17 and 32%, respectively. A triazine ban reduced corn and soybean yields 3 and 20%, respectively, and net farm income by 38%. A simultaneous ban on all dinitroaniline, triazine, and amide herbicides reduced corn and soybean yields 14 and 17%, respectively, and net farm income by 65%. Harvest delays attributable to less effective weed control resulted in a loss in efficiency, yields, and profits, and increased machinery and labor requirements. Hence, herbicide bans under typical Indiana conditions would inflict substantial, short-run economic losses on individual farmers. This study did not measure any potential public benefits or costs of herbicide bans, however.

This research examines the predictability of a profit maximization model, an expected value-variance utility maximization (E-V) model, and two versions of the target-MOTAD model for modeling risky agricultural production decisions. Model solutions were translated into expected value and variance of farm income for analysis. Direct comparison and chi-square analysis of actual and predicted expected income distributions were used in the analyses. It was concluded that the utility maximization and cash-cost target-MOTAD models predicted distributions of farm income better than the variable-cost target-MOTAD and profit maximization models.

An econometric model of the dairy industry was used to estimate the effects on farmers, consumers, and taxpayers of nationwide adoption of the California nonfat solids standards for fluid milk. It was estimated that adoption of the California standards would raise farm-level milk prices by 1 to 5 percent in the short run and by 1 to 2 percent in the long run. The average retail price of fluid milk would rise by 9 to 13 cents per gallon. Dairy program costs fall under most scenarios, but could rise if surpluses fall to levels that would trigger increases in the support price.

This study examines the short- and long-run effects of changes in macroeconomic variables—agricultural commodity prices, interest rates and exchange rates—on the U.S. farm income. For this purpose, we adopt an autoregressive distributed lag (ARDL) approach to cointegration with quarterly data for 1989–2008. Results show that the exchange rate plays a crucial role in determining the long-ran behavior of U.S. farm income, but has little effect in the short-run. We also find that the commodity price and interest rate have been significant determinants of U.S. farm income in both the short- and long-run over the past two decades.

Introduction. Higher allocation of land in favour of fruits is vital to raise the farm income and productivity, but such an opportunity is also complemented with higher risk and uncertainty. Economic expectation assumes a great role, while such decisions have an impact on the welfare of farmers in terms of their income and risk. In this paper, we examined the nature of price expectations, their relationship with other economic factors, and analysed the importance of price and income expectations of the fruit (apple) growers on their land allocation decisions. Materials and methods. In this paper, the elicitation technique was used to obtain both price and income expectations of apple growers. The price expectations were compared with the actual price of apple over the last three years and then linked with farmers' input-use propensities. A regression method was used to identify the role of expectations in the decision of land allocation in favour of apple crops. Results and conclusion. Our results showed that better price expectation improves the input-use (generally labour) propensities. However, for allocation of the inelastic factor of production, i.e., land, in favour of a fruit crop, it is the income expectations that explain farmers’ decisions. Farmers’ capacity to generate higher productivity along with the better market prospects together explain their decision regarding allocating land to high value fruit crops.

In the winter of 2004–2005, over 300 of West Virginia's farmers' market vendors were surveyed with regard to sales levels, promotional techniques and operational characteristics such as hours worked, types of products produced and length of season. Vendors were categorized based on part-time, full-time or retired status, and full-time farmers, both with and without off-farm jobs, were found to be distinct from part-time and retired vendors with respect to 2004 total farmers' market sales and the percentage of household income from farmers' markets. Econometric analysis [ordinary least squares (OLS)] was performed to identify the impact of explanatory variables on total farmers' market sales, percentage of household income from farmers' market sales and amount of household income from farmers' market sales. Independent variables such as bargaining, cost-plus pricing, selling at markets outside West Virginia and providing print materials were found to have a positive impact on annual sales. The number of products produced, distance traveled to market and number of weeks at market were also positively related to the percentage of income obtained from farmers' market sales. Both part-time and retired producers received a lower percentage of household income from farmers' markets relative to full-time producers. Retired and part-time, along with limited-resource vendors (with annual household income less than $20,000) were also found to have lower total sales in the 2004 season. Identifying the characteristics associated with greater farmers' market sales and a higher reliance on such sales for household income will help in the sustained success of markets as engines of economic development and small farm viability.

This study examined the extent and nature of multifamily farms in Iowa. Based on a random sample phone survey conducted in 1997, it was found that over one-fourth (26%) of Iowa's farms would be classified as multifamily farms. Multifamily farms showed significant differences from their single-family farm counterparts in several areas. They tended to have younger operators and larger operations, and the operators were more likely to consider farming as their principal occupation. In addition, they had more diverse operations and generated larger farm and family incomes. The farms were further classified based on their annual sales. As expected, non-commercial farms (sales less than $50,000) had the lowest percentage of multifamily farms (21%). Smallcommercial farms with sales from $50,000 to $250,000 were 30% multifamily, while large-commercial farms were 35% multifamily. The study also addressed whether or not policy restrictions based on age and income would discriminate against farms due to the multifamily farm phenomenon. A logit regression analysis was performed to determine if a multifamily structure was significant in predicting whether or not a farm was small. The results showed that multifamily relations were not a significant predictor. A growing phenomenon in U.S. agriculture, namely the multifamily farm, was examined. A significant percentage of Iowa farms was found to be multifamily. There were many differences between multifamily and single-family farms, but these differences did not display any predictive power. More research is needed in order to understand multifamily farms, a structure that will likely continue to grow and influence U.S. agriculture and outreach programs.