Consumer Preferences for Imported Kona Coffee in South India: A Latent Class Analysis

Considering India as a potential export market for 100% Kona coffee, this study explores consumer preferences for imported, specialty, high-end Kona coffee in South India. Conjoint choice experiment with latent class analysis is used and results indicate that India offers an export market potential for Kona coffee, provided it caters to consumer preferences. Results show a significant preference for strong taste. The relative importance of price is lower than taste but majority are also adverse to higher prices. However,15% of the sample population does not care about price but does care about taste, indicating the possibility of a high-end niche market segment. Based on the results, marketing strategies and policy recommendations have been suggested.India, US Coffee Export, Kona Coffee, Conjoint Choice Experiment, Latent Class Analysis, Agribusiness, Q13,

Farming Fish in a Transitional Economy: A Case for East Timor

This case study evaluates the economic potential for a grow-out mariculture enterprise in East Timor while highlighting how such a business venture could help engage a transitional nation in foreign trade, increase employment opportunities and encourage community based projects that promote sustainable resource use.aquaculture, mariculture, grouper, East Timor, transitional economy, Agricultural and Food Policy, Q10, Q22,

Influence of fetal tissue transplant on the morphology of the neuromuscular junctions of tibialis anterior and medial gastrocnemius following spinal transection in the rat

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Airborne Hyperspectral Data Application in Stress Detection of Blueberry Fields and Ash Trees

Water management and irrigation practices are persistent challenges for many agricultural systems. Changing seasonal and weather patterns impose a greater need for understanding crop deficiencies and excesses (e.g. water, sunlight, nutrients) for optimal growth while allocating proper resources for prompt response. The wild blueberry industry is at heightened susceptibility due to its unique growing conditions and uncultivated nature. Early detection of stress in agricultural fields can prompt management responses to mitigate detrimental conditions including drought and disease. Remote sensing has provided timely and reliable information covering large spatial extents, while novel applications in hyperspectral data and imaging spectroscopy have shown potential in early stress detection. We assess airborne spectral data accompanied by ground sampled water potential over three developmental stages of wild blueberries to accurately detect water content. Airborne scans of spectral data were collected three times throughout the 2019 summer in Deblois, Maine. Data were collected over two adjacent fields, one irrigated and one nonirrigated. Ground sampled data were collected in tandem to the UAV collection. The ground sampled data over the irrigated and non-irrigated fields guided digital sampling from the imagery to act as training for our models. Using methods in machine learning and statistical analysis, we related hyperspectral reflectance measurements to different water potential levels in blueberry plant leaves to decipher vegetation signals both spatially and temporally through utilizing the capacity of imaging spectroscopy. Models were developed to determine irrigation status and water potential. Seven models were assessed in this study with four used to process six hyperspectral cube images for analysis. These images were classified as irrigated or non-irrigated and estimated water potential levels. Our global water potential model had an R2 of 0.62. Models for the water potential predictions were verified with a validation dataset. Forest insect and disease pests have a significant impact on the well-being of individual trees and forest stands, affecting ecosystem processes and potentially human health. Dispersing through 35 states within only 17 years (USDA, 2020), the effect of emerald ash borer (Agrilus Planipennis Fairmaire) (EAB) in the United States has been particularly severe and devastating. Early detection of stress in forests can prompt management responses to mitigate detrimental conditions including drought and disease as well as pest outbreaks. Remote sensing has provided timely and reliable information covering large spatial extents, while novel applications in hyperspectral data and imaging spectroscopy have shown potential in early stress detection. We build on previous work by assessing airborne spectral data, and health classifications of EAB infested ash trees in aims to accurately detect stress. Airborne scans of spectral data were collected within three days in late July 2019 over three sites in southern New Hampshire. Ground sampled data were collected in November 2019 and include sampled ash classified on a scale of 1-5 (1=healthy, no major branch morality, 5=dead). The ground sampled data of different health classifications guided digital sampling from the imagery to act as training and validation for our models. Using methods in machine learning and statistical analysis, we related reflectance measurements to different classifications of ash tree health to understand tree stress signals while utilizing the capacity of remote sensing. Models were developed to classify health in ash trees impacted by EAB. The first entailed a shadow classifier, followed by one for health. Eighteen cube images contained ground sampled data and were processed with the two models, then further buffered. Pixel classification for each buffer sample was calculated. The health classifier model was used on a validation test set and had an prediction accuracy of 76.1%

Trade-offs between Shopping Bags Made of Non-degradable Plastics and Other Materials, Using Latent Class Analysis: The Case of Tianjin, China

white pollution, plastic bag ban, conjoint choice experiment, willingness to pay, latent class analysis, China, degradable plastics, cloth, paper, Community/Rural/Urban Development, Consumer/Household Economics, Environmental Economics and Policy, Q1,

HAWAII PUBLIC OPINION ON AGRICULTURAL PRODUCTS DERIVED FROM GENETICALLY MODIFIED ORGANISM (GMO) TECHNOLOGY

This article studied Hawaii public opinion on agricultural products and processes using GMO technology. We used telephone to interview the people in each island of Hawaii. We found out that the favorability rating toward the attributes of GMO technological application differ based on the nature of GMO benefits. And sociodemographic variables played a significant difference in the preference of using GMO technology on producing agricultural products and process. Most significant associations were gender and island of residence. Age, education and ethnic background significantly also influenced the attitude of respondents toward some of GMO attributes. The fewest significant associations were heard of GMO and income.Consumer/Household Economics, Research and Development/Tech Change/Emerging Technologies,

Analysis of Farm Household Preferences in the Management of Invasive Species: The Case of Miconia in Hawaii

A major threat to Hawaii's ecosystem is the spread of invasive plant species. One such species is Miconia calvescens. Given that this plant was originally introduced to Hawaii by the horticulture industry and has negative effects on agricultural productivity, it is logical to find the farm households' preference for the control of Miconia. Using Conjoint Choice Experiment methodology, this study designed a survey to measure farm households' preferences for Miconia calvescens control program attributes. Results of the surveys indicate that the farm households are willing to support Miconia control programs if they prevent severe soil erosion and loss of biodiversity.Miconia, invasive species, Hawaii, farmers, Conjoint Choice Experiment, valuation, Crop Production/Industries,