Adoption of double-cropping soybeans and wheat

Double-cropping of soybeans and wheat is often promoted by extension personnel. This paper seeks to explain how the decision to adopt double-cropping is made, using a Tobit regression model. Tobit makes use of more of the information in the data set than do logit or probit and explains not only the decision to double-crop but also the rate of adoption. The paper considers factors such as profit and risk perceptions and risk which have not been included in the past models used to explain adoption of technology. The results show that risk perception is important. Contrary to the findings of some other adoption studies, this decision is not influenced by human capital factors. The farmers who double-crop are more highly leveraged and appear to do so both to achieve higher income and as part of a risk diversification strategy. This is consistent with the importance of the location factor, measured as the average number of growing degree days is a proxy for the actual distribution of returns from double-cropping and is the main factor explaining this decision. Extensive adoption of double-cropping in cooler regions of the midwest must await technological advances that can increase the profitability of double-cropping by reducing the growing season for wheat and/or beans

Economic Impacts of Changes in United States Rice Price Variability on Market Efficiency, Marketing Margin and Producer Viability.

20 p

A Cox parametric bootstrap test of the von Liebig hypotheses

This study uses a Cox parametric bootstrap test to select between two specifications of the von Liebig hypothesis, a switching regression (SR) model, and a linear response function with a stochastic plateau. Specifying the production function as a linear response function with a stochastic plateau yields a superior approximation of the data for livestock gain as a function of forage allowance than the SR approach

Cointegration and causality in international agricultural economics research

A review of literature on applications of Granger causality to problems in international agricultural economics research is summarized. The review relates to cointegration theory, and it identifies the areas where recent econometric developments may be of value. Testing procedures are outlined, and a discussion is provided on questions such as non-stationarity and asymptotic distribution of non-causality tests, the relationship between cointegration and causation, the relative merits of various testing procedures, and concerns about testing bivariate causality in higher dimensional models. Finally, a recent econometric development is discussed and its future use in applied research is discussed

The Performance of Agricultural Market Advisory Services in Corn and Soybeans

The purpose of this article is to evaluate the performance of market advisory services for the 1995–2003 corn and soybean crops. A new database from the Agricultural Market Advisory Services (AgMAS) Project is used in the evaluation. This database should not be subject to survivorship and hindsight biases. Overall, the results provide little evidence that advisory services as a group outperform market benchmarks, particularly after considering risk. The evidence is more positive versus the farmer benchmarks, even after taking risk into account. Results also suggest that it is difficult to predict the pricing performance of advisory services across crop-years. Copyright 2006, Oxford University Press.