Inland valley wetlands in West African countries are considered to hold immense potential for rice-based production systems. Policies to increase rice production in inland valleys have until now not lived up to expectations. Previous studies have examined biophysical factors that affect the adoption of inland valley farming, but little empirical work has explored the socio-economic drivers of adoption. This study explores the determinants of farmers’ decisions to adopt inland valley farming and rice production in Côte d’Ivoire and Ghana using probit model with data collected from 742 farmers. We show that owners of perennial tree crops are less likely to adopt inland valley farming and rice production than non-owners. This could be because perennial tree crops yield higher economic returns and provide financial stability, also for the next generation, than inland valley farming and rice production. Furthermore, farm size positively correlates with inland valley farming, but households with larger farms tend not to opt for rice production. Our results underscore that the relation between farm size and agricultural production decisions may depend on the type of agricultural practice. These results suggest that policymakers could strengthen local institutions and service providers to target specific groups of farmers when promoting inland valley farming and rice production.

The classical trinity of tests is used to check for the presence of a tremble in economic experiments in which the response variable is binary. A tremble is said to occur when an agent makes a decision completely at random, without regard to the values taken by the explanatory variables. The properties of the tests are discussed, and an extension of the methodology is used to test for the presence of a tremble in binary panel data from a well-known economic experiment.

Pesticide handling is a critical component of many food supply chains yet labor markets for pesticide handlers are little studied. This study uses data from the U.S. national survey to show that relative to other farmworkers, pesticide handlers get paid 15% more. To understand this premium, matching techniques are used to identify workers who are observationally equivalent in every way except pesticide handling. Using these methods, approximately half of the wage premium can be related back to observable characteristics, including crop type, geographic location, legal work authorization, education, experience, and other personal characteristics.

Tennessee cattle producer willingness to participate in a hypothetical Tennessee Branded Beef Program (TBBP) was examined using 2016 survey data. Willingness to participate in the TBBP was modeled using a probit model. Among those willing to participate, a Tobit model was used to estimate the pounds of live-weight beef producers were willing to supply into a TBBP. Age, production practices, and risk attitudes influenced willingness to participate. Among those willing to participate, projected TBBP supply per farm averaged 32,329 pounds and was influenced by on-farm animal units, production practices, perceived barriers, risk attitudes, and consequentiality beliefs.

The willingness to plant identity preserved (IP) crops was examined using Mississippi soybean producers as an example. A contingent valuation framework was used to assess the impacts of offered premiums on a producer's probability of planting IP soybeans. Findings suggest that offered premiums significantly affect planting decisions. In addition, desire to learn more about IP production was found to increase the probability of planting, suggesting that desire to learn leads to experimentation. Finally, prior knowledge or experience planting IP crops significantly decreased the probability of planting.

Livestock risk protection (LRP) insurance is a price risk management tool available to cattle producers; however, producers have been hesitant to adopt LRP. The objective of the study was to determine the monthly feeder cattle LRP contract coverage level and length maximizing the probability of the LRP net price being greater than the CME Feeder Cattle Index (CME FCI) price. The CME FCI prices were higher than the LRP net price for the majority of the contract lengths and coverage levels. Several coverage lengths and levels provided similar price protection, and there was no consistent preferred coverage length and level.

A credit scoring function incorporating statistical selection criteria was proposed to evaluate the credit worthiness of agricultural cooperative loans in the Fifth Farm Credit District. In-sample (1981-1986) and out-of-sample (1988) prediction performance of the selected models were evaluated using rank transformation discriminant analysis, logit, and probit. Results indicate superior out-of-sample performance for the management oriented approach relative to classification of unacceptable loans, and poor performance of the rank transformation in out-of-sample prediction.

Whole farm simulation analysis and econometric techniques are employed in an analysis of crop rotations and tenure arrangement strategies. The FLIPSIM model is used to analyze a representative Texas Upper Gulf Coast rice and soybean farm. Probit analysis is then used to determine the impact of net cash farm income, land tenure, and crop rotation on probability of survival. Results suggest that, although the simulation model is useful in providing information on the effect at the farm level of following the different strategies, probit results provide greater understanding into the returns and risk inherent to each strategy.

This study provides an analysis of nonindustrial private forest (NIPF) landowners' participation in forestry assistance programs. A probit model was used for data collected from a random sample of 329 Indiana landowners. The analysis revealed that total land owned, commercial reasons for ownership, government sources of information, and membership in forestry organizations influenced NIPF landowners' program participation. Age, fear of loss of property rights, and duration since the first wooded tract was acquired also influenced program participation. Location of landowners' residence on their wooded land and landowners' knowledge of and willingness to participate in a conservation easement influenced the participation in cost-share programs.

A structural probit model is estimated to determine the change in the probability of selecting a milk handler. Cooperatives are thought to have lower prices and higher deductions than independent milk handlers and these factors reduce the probability that a farmer will select a cooperative by 0.39 and 0.32. Cooperatives are thought to have better services and an assured market and payment than independent milk handlers and these factors increase the probability that a farmer will select a cooperative by 0.20 and 0.26. This indicates that many cooperative members value monetary characteristics over non-monetary characteristics.

Probit analysis identified factors that influence the adoption of precision farming technologies by Southeastern cotton farmers. Younger, more educated farmers who operated larger farms and were optimistic about the future of precision farming were most likely to adopt site-specific information technology. The probability of adopting variable-rate input application technology was higher for younger farmers who operated larger farms, owned more of the land they farmed, were more informed about the costs and benefits of precision farming, and were optimistic about the future of precision farming. Computer use was not important, possibly because custom hiring shifts the burden of computer use to agribusiness firms.

This study uses a bivariate probit model with partial observability to examine Louisiana beef producers' awareness of the Environmental Quality Incentives Program (EQIP) and how awareness translates to application to the program. Results indicate that awareness of and application to the EQIP depend on portion of income derived from off-farm sources, extent of previous best management practice adoption at one's own expense, household income, farmed land that is highly erodible, contact with Natural Resource Conservation Service and extension service personnel, and producer age.

With financial modelling requiring a better understanding of model risk, it is helpful to be able to vary assumptions about underlying probability distributions in an efficient manner, preferably without the noise induced by resampling distributions managed by Monte Carlo methods. This paper presents differential equations and solution methods for the functions of the form Q(x) = F−1(G(x)), where F and G are cumulative distribution functions. Such functions allow the direct recycling of Monte Carlo samples from one distribution into samples from another. The method may be developed analytically for certain special cases, and illuminate the idea that it is a more precise form of the traditional Cornish–Fisher expansion. In this manner the model risk of distributional risk may be assessed free of the Monte Carlo noise associated with resampling. The method may also be regarded as providing both analytical and numerical bases for doing more precise Cornish–Fisher transformations. Examples are given of equations for converting normal samples to Student t, and converting exponential to normal. In the case of the normal distribution, the change of variables employed allows the sampling to take place to good accuracy based on a single rational approximation over a very wide range of sample space. The avoidance of branching statements is of use in optimal graphics processing unit (GPU) computations as it avoids the effect of branch divergence. We give a branch-free normal quantile that offers performance improvements in a GPU environment while retaining the best precision characteristics of well-known methods. We also offer models with low probability branch divergence. Comparisons of new and existing forms are made on Nvidia GeForce GTX Titan and Tesla C2050 GPUs. We argue that in both single- and double-precisions, the change-of-variables approach offers the most GPU-optimal Gaussian quantile yet, working faster than the Cuda 5.5 built-in function.

Oil prices rose sharply prior to the onset of the 2007–2009 recession. Hamilton [in the Palgrave Dictionary of Macroeconomics (2008)] noted that nine of the last ten recessions in the United States were preceded by a substantial increases in the price of oil. In this paper, we consider whether oil price shocks significantly increase the probability of recessions in a number of countries. Because business cycle turning points generally are not available for other countries, we estimate the turning points together with oil's effect in a Markov-switching model with time-varying transition probabilities. We find that, for most countries, oil shocks do affect the likelihood of entering a recession. In particular, for a constant, zero-term spread, an average-sized shock to WTI oil prices increases the probability of recession in the United States by nearly 50 percentage points after one year and nearly 90 percentage points after two years.