NORTHLAND FOODS: PLANNING THE END A DECISION CASE

Agribusiness,

INTRODUCTION TO FARM RECORDS AND ACCOUNTING

Agricultural Finance,

Minnesota Agricultural Economist 696

Agricultural Finance,

PRECISION AGRICULTURE: CURRENT ECONOMIC AND ENVIRONMENTAL ISSUES

Research and Development/Tech Change/Emerging Technologies,

IMPROVING THE EVALUATION OF FARM ACCOUNTING SOFTWARE

Agricultural Finance,

ON COMPARING FARM MANAGEMENT ASSOCIATIONS AND THE FARM POPULATION

Farm Management,

A COMPARISON OF MINNESOTA'S FARM BUSINESS MANAGEMENT ASSOCIATION MEMBERS AND THE USDA'S FARM COSTS AND RETURNS SURVEY

Many states have farm record associations which collect individual farm data. This data are used for research, extension, and teaching purposes. However, since membership in the associations is voluntary, the question arises whether the members are representative of the population of all farmers in that area. This study compares farm record data collected through the Southeastern and Southwestern Minnesota Farm Business Management Associations (FBMA) and data obtained through the USDA's Farm Costs and Returns Survey (FCRS). Both data sets were for 1987. By design, the FCRS survey is not subject to the self-selection bias that may occur in the FBMA data. The objectives of this study are to: (1) determine which farm characteristics are statistically the same in the FBMA and FCRS data, and (2) determine the farm size ranges in which FBMA farms are statistically representative of FCRS farms'. FBMA farms were not representative of all farms in their area. FBMA farms do not include small operations. Major differences exist in total tillable acreage, rented land and livestock production, especially hogs. These combined differences result in a substantial difference in net farm income between the two farm categories. However, the FBMA farms reflect FCRS farms' solvency conditions relatively well. FBMA farms were more similar to farms with sales exceeding $60,000 per year but differences still existed. Total acreage, total sales (especially sales of hogs), total expenses, and net farm income were significantly (p<.01) higher for FBMA farms. Even at higher sales levels, FBMA farms were characterized by a higher level of livestock production and a slightly larger tillable acreage mainly due to renting additional land. Economic performance measured by net farm income and returns to total assets and family labor also was significantly (p<.01) better for FBMA farms. So even though differences in assets, liabilities, and thus solvency positions were insignificant (p>.10), the economic performance of the FBMA farms appears to be better than FCRS farms even in larger sizes. On the basis of these findings, the FBMA data cannot be used to represent all farms or even all commercial farms. It does appear that FBMA farms can be used to represent larger farms with livestock. Thus, the FBMA data is not well-suited for estimation of economic relationships to be used in aggregate economic analyses of the agricultural sector.Farm Management,

EXTERNALITIES, RISK AND THE PRIVATE PROPERTY-OVERGRAZING PARADOX: THE CASE OF PRIVATE CATTLE FARMS IN NYABUSHOZI COUNTY, WESTERN UGANDA

Livestock Production/Industries,

THEME OVERVIEW: FUNDAMENTAL FORCES AFFECTING AGRIBUSINESS INDUSTRIES

Agribusiness, Market Forces, Structural Change, Porter’s Five Forces, Community/Rural/Urban Development, Q13, L10, L22, M22, L80,

Economic Efficiency and Factors Explaining Differences Between Minnesota Farm Households

Inter-firm differences in economic efficiency are major factors explaining differences in firm survival and growth and changes in industry structure. Thus, factors explaining differences in efficiency are of major interest to many involved in or affected by the industry. This study was undertaken to improve our understanding of the inter-farm differences in farm household efficiency in utilizing their land, labor, and capital resources to achieve household objectives. It estimated the technical, allocative, and scale efficiencies of farm households in southern Minnesota using a nonparametric, output-based data envelopment analysis (DEA) of a time series, panel dataset from 1993-2005. A bootstrap method was used to establish statistical properties of the technical efficiency estimates. Tobit analysis was conducted to identify significant factors explaining differences in efficiency scores. This study also expands the use of efficiency estimation and Tobit analysis of factors to identify educational needs for improving efficiencies. This study extends current research in several ways. It uses a true panel dataset versus the pseudo panel used by Morrison Paul et al (2004). To our knowledge, this study is the first estimating U.S. agricultural production efficiencies to use bootstrapping procedures to correct the bias generated by the deterministic DEA approach. It is the first to use a weighted Tobit procedure to correct for that same bias. The study is also the first to extend the results of estimating efficiencies and the Tobit identification of explanatory factors to identifying both potential policies and educational opportunities for improving efficiencies. While most previous studies did not consider nonfarm income and labor in their study, the fact that nonfarm activity now accounts for a large percentage of household income and resources means that they should be incorporated in the calculating of production frontier. As in Morrison Paul et al. (2004) and Chavas et al. (2005), this study incorporated nonfarm income as an output and nonfarm labor as an input in the production technology. Following Chavas et al. (2005), Morrison Paul et al. (2004), and others, we first used nonparametric (DEA) methods to estimate output-based technical, allocative, and scale efficiencies. Based on the smoothed bootstrap procedure for DEA estimators proposed by Simar and Wilson (1998, 2000), the study estimated the bias and the confidence interval of the DEA estimators for TE. Initial technical efficiency was estimated to be 0.90; scale efficiency to be 0.88, and allocative efficiency to be 0.77. These efficiencies improved over the period. Using the bootstrapping results, the average bias-corrected technical efficiency was 0.77 or 86% of the initial estimate. The bias-corrected technical efficiencies were similar for small farms and large farms even though the difference between the lower and upper bounds was larger for large farms. Tobit analysis showed that more specialized farms had higher levels of efficiency by all three measures. A higher proportion of rented land was associated with higher allocative and scale efficiency but lower technical efficiency. A lower debt-asset ratio and a higher percentage of current debt were associated with higher levels of all three measures of farm efficiency. A higher proportion of nonfarm household income and higher hired labor, capital-to-labor and land-to-labor ratios had positive effects on efficiency. Several conclusions and suggestions for improving farm efficiencies can be drawn from these results. First, since a lower debt-asset ratio and higher percentage of current debt were associated with higher efficiency levels, management skills that improve financial condition will likely improve efficiency. So improvement of management skills in general, through education of current and future farmers, appears to be needed (as we have always striven to do). Increasing the amount of rented land relative to owned land has a positive impact on allocative and scale efficiency so improved land markets and the ability to obtain and hold additional land is critical. Also, improvement in land market negotiation skills and intra-personal skills dealing with absentee landowners can lead to efficiency improvements. However, since a higher tenancy ratio was associated with lower technical efficiency, improvements in managing larger operations and rented properties appears to be needed. The positive impact of nonfarm income shows the need for farm households to take advantage of nonfarm opportunities as well as the need for rural communities to expand and develop those opportunities. Better access to both debt and nonfarm equity capital can improve efficiencies. This includes the identification and use of nonfarm capital (such as partnerships and investments by nonfarmers) and the identification and use of lower cost-debt capital for expansion and improvements as well as the increased management ability to manage higher debt loads. The positive impact of higher capital-to-labor and land-to-labor ratios indicates the need for more intensive use of available labor through increased mechanization and expansion of the land base. These steps can be seen as needing to accompany the ability to access more debt and equity capital. The positive hired labor ratio illustrates the impact of hiring labor and thus, presumably, freeing the farm household to spend more time on management following the highest and best use argument for the owner's time allocation. The need to increase the relative amount of hired labor points to the need to increase personnel management ability in farmers and thus personnel management educational opportunities for current and future farmers. The positive impact of the Herfindahl index shows the need to increase management skills, and risk management skills especially, to handle more specialized operations that will rely on off-farm tools for protection from uncertainty versus relying on on-farm diversification as a risk decreasing tool. For the AAEA meetings, the presentation will include an explanation of the estimation methods used including the smoothed bootstrapping methods (Simar and Wilson). Quantitative results will be presented including the initial TE estimates compared to the bias corrected TE estimates. The significant explanatory factors identified through in the Tobit analysis will be presented. We will expand our discussion of the educational needs identified for improving efficiencies. We anticipate discussion will occur on the use of the bootstrapping method and our identification of education needs for improving household efficiency. Chavas, J-P, Petrie, R., and Roth, M. (2005). �Farm Household Production Efficiency: Evidence from The Gambia.� American Journal of Agricultural Economics 87 (1): 160-179. Morrison Paul, C., Nehring, R., Banker D. and Somwaru A. (2004). �Scale Economics and Efficiency in U.S. Agriculture: Are Traditional Farms History?� Journal of Productivity Analysis, 22 (2004):185-205. Simar, L., and P., Wilson (1998), �Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models.� Management Science 44(1): 49-61. Simar, L., and P., Wilson (2000), A general methodology for bootstrapping in nonparametric frontier models, Journal of Applied Statistics, 27(6):779-802.Productivity Analysis,