This paper investigates the factors that drive farmers’ simultaneous adoption of six water conservation practices (WCPs) and the intensity of their adoption. We estimate farmers’ adoption of these WCPs with a multivariate probit model, and for the intensity of their adoption, an ordered probit model is estimated. Our results show that gender, age, education, and farm size (among other factors) influence the probability and extent of adoption of WCPs. Furthermore, combinations like drip and/or sprinkler irrigations and cover cropping, drip and/or sprinkler irrigations and intercropping (among others) are complements, suggesting the bundling of these WCPs.

Bolivia has disseminated several improved technologies in the rice sector, but the average rice productivity in the country is far below the average trend in Latin America in recent years. Although the economic literature has highlighted the role of agricultural technology adoption in increasing agricultural productivity, gaps remain in understanding how rice growers are deciding to adopt and benefit from available improved rice technologies. Most previous adoption studies have evaluated the uptake of individual technologies without paying attention to the complementarities that alternative improved rice technologies may offer to farmers who face multiple marketing and production needs. This study uses data from a nationally representative sample of Bolivian rice growers to analyze farmers' joint decisions in adopting complementary agricultural technologies controlling for potential correlations across these decisions, as well as the extent of adoption of these practices. Evidence suggests that the decisions on multiple technology adoption are closely related, with common factors affecting both adoption and the extent of adoption. Furthermore, there is a need to better target resource-poor farmers, improve information-diffusion channels on agricultural practices, and better use existing farmers' organizations to enhance rice technology adoption.

While logistic regression models are easily accessible to researchers, when applied to network data there are unrealistic assumptions made about the dependence structure of the data. For temporal networks measured in discrete time, recent work has made good advances (Almquist & Butts, 2014), but there is still the assumption that the dyads are conditionally independent given the edge histories. This assumption can be quite strong and is sometimes difficult to justify. If time steps are rather large, one would typically expect not only the existence of temporal dependencies among the dyads across observed time points but also the existence of simultaneous dependencies affecting how the dyads of the network co-evolve. We propose a general observation-driven model for dynamic networks that overcomes this problem by modeling both the mean and the covariance structures as functions of the edge histories using a flexible autoregressive approach. This approach can be shown to fit into a generalized linear mixed model framework. We propose a visualization method that provides evidence concerning the existence of simultaneous dependence. We describe a simulation study to determine the method's performance in the presence and absence of simultaneous dependence, and we analyze both a proximity network from conference attendees and a world trade network. We also use this last data set to illustrate how simultaneous dependencies become more prominent as the time intervals become coarser.

Data on owner and land characteristics are used to analyze factors affecting participation decisions in Delaware's agricultural lands preservation program, federal commodity programs, and federal conservation programs. A trivariate probit model estimates a set of random utility models of participation. Participation decisions at the state and federal levels are found to be driven by many of the same observed factors, but uncorrelated in unobserved characteristics. The important exceptions are that owners of small parcels under development pressure and with parcels of relatively low environmental quality tend to enroll in commodity programs rather than preservation. In part, the complex policy environment may therefore limit the effectiveness of programs seeking to preserve parcels with the highest environmental quality or facing the greatest development pressure.

Precision farming information demanded by cotton producers is provided by various suppliers, including consultants, farm input dealerships, University Extension systems, and media sources. Factors associated with the decisions to select among information sources to search for precision farming information are analyzed using a multivariate probit regression accounting for correlation among the different selection decisions. Factors influencing these decisions are age, education, and income. These findings should be valuable to precision farming information providers who may be able to better meet their target clientele needs.

An important aspect of structural change in the U.S. hog industry has been the adoption of breeding technology. The adoption of each of four breeding technologies, weekly farrowing, intensive breeding, terminal crossbreeding, and artificial insemination, in U.S. hog production is analyzed using multivariate probit analysis. Results suggest that diversification, whether the producer raised breeding stock, debt and asset levels, and producer's education influence adoption rate. Larger, more risk-averse producers were more likely to adopt technology. Producers who rated the quality of labor available as higher were more likely to adopt management-intensive technologies.

Factors affecting the adoption of crop insurance, forward contracting, and spreading sales are analyzed using multivariate and multinomial probit approaches that account for simultaneous adoption and/or correlation among the three risk management adoption decisions. Our empirical results suggest that the decision to adopt crop insurance, forward contracting, and/or spreading sales are correlated. Richer insights can be drawn from our multivariate and multinomial probit analysis than from separate, single-equation probit estimation that assumes independence of adoption decisions. Some factors significantly affecting the adoption of the risk management tools analyzed are proportion of owned acres, off-farm income, education, age, and level of business risks.