Dataset Information

The Effects of Extreme Weather on Apple Quality.

ABSTRACT: A large literature has documented the effects of weather on agricultural yields. However, weather not only impacts the quantity produced, but also the quality of the product. Due to data limitations, the quality effects have primarily been studied using lab experiments for specific attributes, and the financial implications for farmers of a quality effect are less clear. Using a unique longitudinal micro-level data set of Swiss apple orchards that include information on both the quantity produced as well as the quality, we show that the latter can have an even larger effect on farm revenue. Ignoring the quality of the harvested product substantially biases the impact of weather extremes on agricultural income and the potential effects of climate change. Our quality measure is the orchard-year specific price shock. If an orchard gets a lower price for its specific apple variety compared to previous years and compared to other orchards in the same year, we observe the market's valuation of its inferior quality accounting for overall price movements (other orchards growing same variety that year) as well as orchard specific factors (orchard fixed effects). We find that spring frost events induce farm gate price drops and thus revenue reductions of up to 2.05% per hour of exposure.

SUBMITTER: Dalhaus T

PROVIDER: S-EPMC7220945 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Effects of Extreme Weather on Apple Quality.

Dalhaus Tobias T Schlenker Wolfram W Blanke Michael M MM Bravin Esther E Finger Robert R

Scientific reports 20200513 1

A large literature has documented the effects of weather on agricultural yields. However, weather not only impacts the quantity produced, but also the quality of the product. Due to data limitations, the quality effects have primarily been studied using lab experiments for specific attributes, and the financial implications for farmers of a quality effect are less clear. Using a unique longitudinal micro-level data set of Swiss apple orchards that include information on both the quantity produce ...[more]

PMID: 32404968

Similar Datasets

Project description:While many authors have described the adverse health effects of poor air quality and meteorological extremes, there remain inconsistencies on a regional scale as well as uncertainty about the single and joint effects of atmospheric predictors. In this context, we investigated the short-term impacts of weather and air quality on moderate extreme cancer-related mortality events for the urban area of Augsburg, Southern Germany, during the period 2000-2017. First, single effects were uncovered by applying a case-crossover routine. The overall impact was assessed by performing a Mann-Whitney U testing scheme. We then compared the results of this procedure to extreme noncancer-related mortality events. In a second step, we found periods with contemporaneous significant predictors and carried out an in-depth analysis of these joint-effect periods. We were interested in the atmospheric processes leading to the emergence of significant conditions. Hence, we applied the Principal Component Analysis to large-scale synoptic conditions during these periods. The results demonstrate a strong linkage between high-mortality events in cancer patients and significantly above-average levels of nitrogen dioxide (NO2) and particulate matter (PM2.5) during the late winter through spring period. These were mainly linked to northerly to easterly weak airflow under stable, high-pressure conditions. Especially in winter and spring, this can result in low temperatures and a ground-level increase and the accumulation of air pollution from heating and traffic as well as eastern lateral advection of polluted air. Additionally, above-average temperatures were shown to occur on the days before mortality events from mid-summer through fall, which was also caused by high-pressure conditions with weak wind flow and intense solar radiation. Our approach can be used to analyse medical data with epidemiological as well as climatological methods while providing a more vivid representation of the underlying atmospheric processes.

Project description:At a global level, climate change is expected to result in more frequent and higher-intensity weather events, with impacts ranging from inconvenient to catastrophic. The potential for disasters to act as "focusing events" for policy change, including adaptation to climate change risk, is well known. Moreover, local action is an important element of climate change adaptation and related risk management efforts. As such, there is a good reason to expect local communities to mobilize in response to disaster events, both with immediate response and recovery-focused activities, as well as longer-term preparedness and adaptation-focused public policy changes. However, scholars also note that the experience of disaster does not always yield policy change; indeed, disasters can also result in policy inertia and failure, perhaps as often or more often than major policy change. This study poses two key research questions. First, we ask to what degree policy change occurs in communities impacted by an extreme weather event. Second, we seek to understand the conditions that lead to adaptation-oriented policy adoption in response to an extreme weather event. Our results suggest two main recipes for future-oriented policy adoption in the wake of an extreme weather event. For both recipes, a high-impact event is a necessary condition for future-oriented policy adoption. In the first recipe for change, policy adoption occurs in Democratic communities with highly focused media attention. The second, less expected recipe for change involves Republican communities that have experienced other uncommon weather events in the recent past. We use a comparative case approach with 15 cases and fuzzy set qualitative comparative analysis methods. Our approach adds to the existing literature on policy change and local adaptation by selecting a mid-N range of cases where extreme weather events have the potential to act as focusing events, thereby sidestepping selection on the dependent variable. Our approach also takes advantage of a novel method for measuring attention, the latent Dirichlet allocation approach.

Dataset Information

The Effects of Extreme Weather on Apple Quality.

Publications

The Effects of Extreme Weather on Apple Quality.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets