Best management practices (BMPs) for reducing agricultural non-point source pollution are widely available. However, agriculture remains a major global contributor to degradation of waters because farmers often do not adopt BMPs. To improve water quality, it is necessary to understand the factors that influence BMP adoption by farmers. We review the findings of BMP adoption studies from both developed and developing countries, published after (or otherwise not included in) two major literature reviews from 2007 and 2008. We summarize the study locations, scales, and BMPs studied; the analytical methods used; the factors evaluated; and the directionality of each factor’s influence on BMP adoption. We then present a conceptual framework for BMP adoption decisions that emphasizes the importance of scale, the tailoring or targeting of information and incentives, and the importance of expected farm profits. We suggest that future research directions should focus on study scale, on measuring and modeling of adoption as a continuous process, and on incorporation of social norms and uncertainty into decision-making. More research is needed on uses of social media and market recognition approaches (such as certificate schemes and consumer labeling) to influence BMP adoption.

In this paper, the random forests method and the support vector machine in machine learning are explored and compared to the traditional statistical-based maximum likelihood method with 126 features from Sentinel-2A images. The spectral reflectance of 12 bands, 96 texture parameters, 7 vegetation indices, and 11 phenological parameters are successfully extracted from Sentinel-2A images in 2017. The classification result shows that the optimal combination of 13 features yields overall accuracies of traditional classification and machine learning classification of 88.96% and 98%, respectively. Short-wave infrared information shows a significant effect on distinguishing rice, corn, and soybean. The water vapor band plays a significant role in distinguishing between corn and rice. In the multiclassification problem, the machine learning methods have robustness with the identification accuracy of greater than 95% for each crop type, whereas the traditional classification result shows imbalanced accuracies for different crops.

We examine the impact of water quality in Narragansett Bay on housing prices in coastal towns and cities using a hedonic housing‐price model. Unlike other hedonic studies of water quality, we test whether housing market responds to average water quality or more to extreme events. We also test the spatial and temporal extent of effects of water quality on housing prices. We find that poor coastal water quality, measured in terms of the concentration of chlorophyll, has a negative impact on housing prices that diminishes with distance from the shoreline. Furthermore, our finding suggests that housing prices are most influenced by the extreme environmental conditions, which may be accompanied by unpleasant odors, discoloration, and even fish kills. We further predict potential increases in home values associated under water quality improvement scenarios and find an increase in the values of homes in coastal communities along Narragansett Bay of about $18 million up to $136 million.

This study spatially quantifies hydrological ecosystem services and the production of ecosystem services at the watershed scale. We also investigate the effects of stressors such as land use change, climate change, and choices in land management practices on production of ecosystem services and their values. We demonstrate the approach in the Beaver River watershed in Rhode Island. Our key finding is that choices in land use and land management practices create tradeoffs across multiple ecosystem services and the extent of these tradeoffs depends considerably on the scenarios and ecosystem services being compared.

This paper reviews previous efforts to assign monetary value to climatic or meteorological information, such as public information on drought, climate, early warning systems, and weather forecast information. Methods and tools that have been explored to examine the benefits of climatic and meteorological information include the avoided cost, contingent valuation, choice experiments, benefit transfer, and descriptive approaches using surveys. The second part of this paper discusses specific considerations related to valuing drought information for public health and the Bureau of Land Management. We found a multitude of connections between drought and the land management and health sectors in the literature. The majority of the papers that we summarized only report biophysical change, because the economic losses of drought are not available. Only a few papers reported economic loss associated with drought. To determine the value of drought information, we need to know more about the role it plays in decision making and what sources of drought information are used in different sectors. This inventory of methods and impacts highlights opportunities for further research in valuing drought information in land management and public health.

Droughts affect recreation and tourism, grazing, forests, and timber, and can have important indirect effects for the ecosystems and species that rely on water. Despite its importance, the effect of drought in the land management sector is less understood than in other water-intensive sectors, such as agriculture and public water supplies. This study presents the first-ever estimates of the economic valuation of the information provided by the U.S. Drought Monitor using the avoided cost method. These estimates are based on the time and labor saved by using the U.S. Drought Monitor rather than compiling drought-related information from other sources, or using other sources for tracking/monitoring droughts, communicating drought conditions, and dealing with drought-related issues. The results reflect rational behavior—the more time needed to compile or collect drought information provided by the U.S. Drought Monitor, the higher the dollar value in avoided cost. This dollar amount also varies by institution and organization, which indicates respondents from different organizations value the information from the U.S. Drought Monitor differently. For example, compared to the state offices, the field offices in the Bureau of Land Management value more of the information provided by the U.S. Drought Monitor. These estimates can be used to estimate the societal benefits and help policy makers evaluate the U.S. Drought Monitor in different sectors.