227 research outputs found

    A robust Bayesian analysis of the impact of policy decisions on crop rotations.

    Get PDF
    We analyse the impact of a policy decision on crop rotations, using the imprecise land use model that was developed by the authors in earlier work. A specific challenge in crop rotation models is that farmer’s crop choices are driven by both policy changes and external non-stationary factors, such as rainfall, temperature and agricultural input and output prices. Such dynamics can be modelled by a non-stationary stochastic process, where crop transition probabilities are multinomial logistic functions of such external factors. We use a robust Bayesian approach to estimate the parameters of our model, and validate it by comparing the model response with a non-parametric estimate, as well as by cross validation. Finally, we use the resulting predictions to solve a hypothetical yet realistic policy problem

    Logistic regression on Markov chains for crop rotation modelling.

    Get PDF
    Often, in dynamical systems, such as farmer's crop choices, the dynamics is driven by external non-stationary factors, such as rainfall, temperature, and economy. Such dynamics can be modelled by a non-stationary Markov chain, where the transition probabilities are logistic functions of such external factors. We investigate the problem of estimating the parameters of the logistic model from data, using conjugate analysis with a fairly broad class of priors, to accommodate scarcity of data and lack of strong prior expert opinions. We show how maximum likelihood methods can be used to get bounds on the posterior mode of the parameters

    A robust Bayesian analysis of the impact of policy decisions on crop rotations

    Get PDF
    We analyse the impact of a policy decision on crop rotations, using the imprecise land use model that was developed by the authors in earlier work. A specific challenge in crop rotation models is that farmer’s crop choices are driven by both policy changes and external non-stationary factors, such as rainfall, temperature and agricultural input and output prices. Such dynamics can be modelled by a non-stationary stochastic process, where crop transition probabilities are multinomial logistic functions of such external factors. We use a robust Bayesian approach to estimate the parameters of our model, and validate it by comparing the model response with a non-parametric estimate, as well as by cross validation. Finally, we use the resulting predictions to solve a hypothetical yet realistic policy problem

    A robust Bayesian land use model for crop rotations

    Get PDF
    Often, in dynamical systems, such as farmers’ crop choices, the dynamics are driven by external non-stationary factors, such as rainfall and agricultural input and output prices. Such dynamics can be modelled by a non-stationary stochastic process, where the transition probabilities are functions of such external factors. We propose using a multinomial logit model for these transition probabilities, and investigate the problem of estimating the parameters of this model from data. We adapt the work of Chen and Ibrahim to propose a conjugate prior distribution for the parameters of the multinomial logit model. Inspired by the imprecise Dirichlet model, we will perform a robust Bayesian analysis by proposing a fairly broad class of prior distributions, in order to accommodate scarcity of data and lack of strong prior expert opinion. We discuss the computation of bounds for the posterior transition probabilities, using a variety of calculation methods. These sets of posterior transition probabilities mean that our land use model consists of a non-stationary imprecise stochastic process. We discuss computation of future events in this process. Finally, we use our novel land use model to investigate real-world data. We investigate the impact of external variables on the posterior transition probabilities, and investigate a scenario for future crop growth. We also use our model to solve a hypothetical yet realistic policy problem

    Logistic Regression on Markov Chains for Crop Rotation Modelling

    Get PDF
    Often, in dynamical systems, such as farmer's crop choices, the dynamics is driven by external non-stationary factors, such as rainfall, temperature, and economy. Such dynamics can be modelled by a non-stationary Markov chain, where the transition probabilities are logistic functions of such external factors. We investigate the problem of estimating the parameters of the logistic model from data, using conjugate analysis with a fairly broad class of priors, to accommodate scarcity of data and lack of strong prior expert opinions. We show how maximum likelihood methods can be used to get bounds on the posterior mode of the parameters

    UTILIZING LARGE SCALE DATASETS TO EVALUATE ASPECTS OF A SUSTAINABLE BIOECONOMY

    Get PDF
    This dissertation combines large scale datasets to evaluate crop prediction, land values, and consumption of a crop being considered to advance a sustainable bioeconomy. In chapter 2, we propose a novel application of the multinomial logit (MNL) model to estimate the conditional transition probabilities of crop choice for the state of Kentucky. Utilizing the recovered transition probabilities the forecast distributions of total acreages for alfalfa, corn, soybeans, tobacco, and wheat produced in the state from 2010 to 2015 can be recovered. The Cropland Data Layer is merged with the Common Land Unit dataset to allow for the identification of crop choice at the field level. Our findings show there are higher probabilities of planting soybeans or wheat after corn relative to corn after corn, tobacco, or alfalfa. In addition, the transition probability of the crop rotation demonstrates that corn will be planted after soybean, and vice versa and that alfalfa has a lower probability of being rotated with other crops from year to year. These findings are expected with traditional crop rotation in the U.S., and a characteristic of a perennial crop, especially for alfalfa. Finally, forecasting results indicate that there are significantly wider distributions in corn and soybean, whereas there is a little variation in the tobacco, wheat and alfalfa acres in the simulation. In chapter 3, we identify critical consumer-demographic characteristics that are associated with the consumption of products containing hemp and investigate their effect on total expenditure in the U.S. To estimate the likelihood of market participation and consumption level, the Heckman selection model, is employed using the maximum likelihood estimation procedure utilizing Nielsen consumer panel data from 2008 to 2015. Results indicate marketing strategies targeting consumers with higher education and income levels can attract new customers and increase sales from current consumers for this burgeoning market. Head-of-household age in different regions shows mixed effects on decisions to purchase hemp products and consumption levels. Findings will provide a basic understanding of a consumer profile and overall hemp market that has had double-digit growth over the last six years. As the industry continues to move forward, policymakers are going to need a deeper understanding of the factors driving the industry if they are going to create regulations that support the development of the industry. In chapter 4, we investigate the factors that affect agricultural land values by proposing a new rich dataset, Zillow Transaction and Assessment Data (ZTRAX) provided by Zillow from 2009 to 2014. we also examine whether National Commodity Crop Productivity Index (NCCPI) could be a good indicator of land values or not by comparing two different regression models between county-level cash rent and parcel-level NCCPI. Finally, this study incorporates flexible functional forms of the parcel size to test the parcel size and land values relations. Findings show that factors influencing agricultural land values in states with heterogeneous agricultural lands such as Kentucky are not different from other states with relatively homogeneous agricultural lands. This study also provides suggestive evidence that there is a non-linear relationship between parcel size and land values. Furthermore, we find that a disaggregated NCCPI at parcel-level could be considered an acceptable indicator to estimate agricultural values compared to an aggregated cash rent at county-level

    Binary credal classification under sparsity constraints.

    Get PDF
    Binary classification is a well known problem in statistics. Besides classical methods, several techniques such as the naive credal classifier (for categorical data) and imprecise logistic regression (for continuous data) have been proposed to handle sparse data. However, a convincing approach to the classification problem in high dimensional problems (i.e., when the number of attributes is larger than the number of observations) is yet to be explored in the context of imprecise probability. In this article, we propose a sensitivity analysis based on penalised logistic regression scheme that works as binary classifier for high dimensional cases. We use an approach based on a set of likelihood functions (i.e. an imprecise likelihood, if you like), that assigns a set of weights to the attributes, to ensure a robust selection of the important attributes, whilst training the model at the same time, all in one fell swoop. We do a sensitivity analysis on the weights of the penalty term resulting in a set of sparse constraints which helps to identify imprecision in the dataset

    Demographic consequences of agricultural practices on a long-lived avian predator

    Get PDF
    Includes bibliographical references.2022 Fall.To view the abstract, please see the full text of the document
    • …
    corecore