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Automatic milking systems (AMSs) are a relatively new technology characterized by uncertainty and irreversibility. The choice to invest in such a system is analyzed in a real options framework. Alternative financing arrangements, depreciation methods, and other factors are investigated to determine their influence on the optimal investment decision. The results suggest that farm capital structure, loan term, and depreciation method have little impact on the investment decision. The primary determinant in the AMS technology adoption decision appears to be whether the AMS will last longer than the existing parlor.

Some past studies of credit risk ratings migration have found trend reversals and evidence that the data‐generating process is nonstationary. Using a sample of Farm Credit System mortgages, we find no compelling statistical evidence of either phenomenon. We do find evidence that our sample of loans may be characterized by two types of borrowers – namely, movers and stayers. This type of borrower heterogeneity is unobserved because movers who do not migrate are indistinguishable from stayers who never migrate. We report on the development of a flexible nonparametric model for estimating transition probabilities. The model can also be used to estimate nonstationary transition probabilities and an example is provided.

For agricultural businesses, managing debt capital means choosing from among myriad sources and terms for financing for inputs, machinery, equipment, and land. Providers of debt capital, including input suppliers, equipment dealers, commercial banks, and the Farm Credit System, offer differing interest rates, rebates, points and other non‐interest costs. Microsoft Excel™‐based financing decision aids were developed to help agricultural decision makers evaluate options by determining the true cost of capital from supplier financing, machinery and equipment financing, and real estate purchases. These same tools were also used as a teaching aid in senior‐level university courses in farm management and agricultural finance to reinforce agricultural cost of capital concepts.

Defaulting on a mortgage represents the ultimate consequence of past decisions to delay payment. While many modeling approaches are available to estimate the probability of default, most if not all require account‐level data. Further, past research has not attempted to estimate the probability that a current loan will transition among delinquency states prior to default. In this paper, we present an econometric approach that makes use of publicly available aggregate data for estimating the probability of delinquency and the probability of default. The results suggest the approach may have merit for monitoring bank performance as well as usefulness for banks’ risk management efforts.

Unlike most residential mortgage‐backed securities, Farmer Mac agricultural mortgage‐backed securities (AMBS) have prepayment penalties. To quantify the impact of prepayment penalties on the cost of funding loans, prepayment behavior, and AMBS value, an analytic AMBS model is developed and numerically solved using pathwise simulation and dynamic programming. The results suggest prepayment penalties currently being used by Farmer Mac reduce AMBS yields considerably relative to not charging prepayment penalties. Presumably, lower yielding AMBS imply more competitive loan pricing. Even with prepayment penalties in place, however, it can be advantageous for profit‐maximizing mortgagors to optimally prepay or even default on agricultural mortgages. The model is used to quantify prepayment and credit risk by valuing the embedded options in mortgages, namely, the right of the mortgagor to prepay and to default.

Passage of the 1996 Farm Bill marked a dramatic departure in federal farm policy as the longstanding deficiency payment program was replaced with non‐risk responsive transition payments. In light of the departure, subsidized savings has been proposed as a mechanism to provide risk protection to agricultural producers. Using Canada’s National Income Stabilization Account (NISA) program as an example of a subsidized savings program, a stochastic programming model of income stabilization is developed. The model is then used to investigate the optimizing behavior of a typical Midwestern crop producer. The results suggest a fair amount of program design flexibility exists, and that the government can use this flexibility to stimulate initial and continual participation while minimizing capital outlays.

A balanced panel data set covering 519 U.S. agricultural banks is constructed for the period 1996S2005. Cost efficiency measures of agricultural banks obtained from stochastic frontier analysis and data envelopment analysis are regressed on various bank specific characteristics to explain the cost efficiency differences of agricultural banks. The results indicate that (a) cost efficiencies are positively related to profitability while negatively related with the raw cost inefficiency measure, (b) older agricultural banks tend to be more efficient, (c) regulation may deteriorate efficiency levels, (d) bigger agricultural banks tend to be less efficient, (e) bank‐specific characteristics can explain DEA efficiency scores better than they can SFA efficiency measures, and (f) the inconsistency issue related to two‐step approaches is not serious.

This paper aims to identify factors that affect agricultural mortgage default and prepayment.

Using a sample of farm credit system loans, prepayment and default are modeled as competing risks with potentially non‐stationary covariates using a statistical/econometric technique called survival snalysis (SA).

The analysis suggests that the primary drivers of prepayment and default are the rate of interest charged by the lender at origination and the borrower's current ratio at origination. Tests of the existence of a geographic effect indicate that despite bank management belief to the contrary, branches may not be homogeneous.

This analysis would be improved if more data were available in an easily obtainable manner to control for unobserved heterogeneity. Unobserved heterogeneity or incomplete specification within a model can be problematic. Inferences among regression coefficients can be problematic in that the estimates have inflated variances and unreliable test statistics. In addition, more frequent measures of the time‐varying covariates could be obtained to improve upon the SA models presented above. Future analyses could also incorporate other sections of the agricultural credit association portfolio, as well as a comparison to variable rate notes. One other logical next step would be to obtain loan collateral values to obtain estimates of the exposure at default, and the loss given default, or the estimates needed for the advanced internal ratings based approach described in the Basel Accords.

This paper provides a method for lenders to measure and model mortgage termination, an important consideration for risk managers when determining capital adequacy described in the Basel Accords.

The purpose of this paper is to study entropy generation in magneto-Jeffrey nanomaterial flow by impermeable moving boundary. Adopted nanomaterial model accounts Brownian and thermophoretic diffusions. Modeling is arranged for thermal radiation, nonlinear convection and viscous dissipation. In addition, the concept of Arrhenius activation energy associated with chemical reaction are introduced for description of mass transportation.

Homotopy algorithms are used to compute the system of ordinary differential equations.

The afore-stated analysis clearly notes that simultaneous aspects of activation energy and entropy generation are not yet investigated. Therefore, the intention here is to consider such effects to formulate and investigate the magneto-Jeffrey nanoliquid flow by impermeable moving surface.

As per the authors’ knowledge, no such work has yet been published in the literature.

This chapter traces the career trajectories of the teachHOUSTON science, technology, engineering, and mathematics (STEM) teachers since the inception of the program. It asks whether the National Science Foundation (NSF) investment of millions of dollars in STEM education produced more STEM teachers of high quality for the diverse, urban area. The chapter is filled with descriptive statistics and stories. Two major findings are that the teachHOUSTON program produced double-digit physics teachers when the Greater Houston area had not had a freshly prepared physics teacher in over a decade. Additionally, teachHOUSTON graduates have distinguished themselves by being named recipients of several awards such as beginning teacher of the year awards, district teacher of the year awards, among other distinctions. teachHOUSTON alumni are also serving in a variety of leadership capacities for high-need public school districts. The chapter ends with a discussion of the program's strengths as well as the areas in which it continues to grow. Three new NSF grants allow for continued improvement and transition this work into two additional books proposed in this three-volume series.