Extreme Events and Climate Change. Группа авторов

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СКАЧАТЬ 4,425,425 Strawberries, lettuce, broccoli, grapes Stanislaus 3,183,150 Almonds, milk, chickens, nursery Merced 3,409,518 Almonds, milk, cattle and calves, chickens San Joaquin 2,527,966 Grapes, almonds, walnuts, milk Ventura 2,099,889 Lemons, strawberries, celery, raspberries Kings 2,066,329 Milk, cotton (Pima), almonds, cattle and calves Imperial 2,065,599 Lettuce, hay (alfalfa), broccoli, cattle and calves

      2.1.2. Employment

      The California agricultural sector generates significant employment, accounting for an average of 420,000 workers engaged in agricultural and livestock activities in 2017, an increase from approximately 370,000 workers in 2005. The great majority (83%) of those workers earned their income from farm employment only (Martin et al., 2017). The California agricultural labor force is aging, with an average age of 38 years old, drawing from families that often include both second‐generation US citizens as well as others who are undocumented or unauthorized to work in the US (Martin et al., 2017). Despite increased mechanization trends for some California‐produced crops, employment in the sector is increasing due to rising production of labor‐intensive crops, mainly berries, over the last two decades (Khan et al., 2004; Martin et al., 2017). This increase has more than offset the decline in production of peaches, apples, and related crops.

      Because of the 2008–2009 recession and the new reality of the Trump administration immigration policies, the flow of new migrant labor into the agricultural sector has slowed. Farmers have had adapt to current labor shortages and the prospect of these shortages could persist in the long term. Some of the responses observed include not only higher wages but also the provision of benefits that, although perhaps not comparable to other economic activities of the diversified California economy, constitute an improvement in employment practices on the part of farmers. However, mechanization, such as the adoption of hydraulic platforms on fruit and tree nut farms, has resulted in underemployment, with the consequent decreases in household income for some. We note that the work presented in later sections of this chapter concentrate on harvesting activities and that the harvesting labor force is mostly temporary as workers seek jobs in the areas where they live and/or move across the state to seek jobs harvesting crops. Nevertheless, worker mobility has declined substantially.

2.2. EXTREME EVENTS AND CLIMATE CHANGE: HEAT INDEX

      No matter how heat waves are defined, the human influence on them is clear (Collins et al., 2013; IPCC, 2011; Min et al., 2013). As the climate continues to warm due to increasing fossil fuel emissions, the severity and frequency of deadly heat waves will continue to increase (Kharin et al., 2013; Wehner et al., 2018). The effect of high temperatures on human beings and other animals is exacerbated by moderate to high humidity (Mora et al., 2017) and a variety of different indices are used to quantify the combined effect of temperature and humidity during heat waves (Wehner et al., 2017).

Heat waves in California are often hot and dry due to stagnant wind conditions. However, some California heat waves, such as the particularly deadly 2006 event, are characterized by more humid conditions, sometimes due to moisture transport from the ocean and gulf waters near Baja California into the Central Valley (Gershunov et al., 2009; Ostro et al., 2009). The widely used Steadman’s heat index (HI; Dahl et al., 2019; Wehner et al., 2017) is a measure of combined extreme heat and humidity (Steadman, 1979a, 1979b). The following bi‐cubic function of surface air temperature (T) and relative humidity (R) is a good fit to tabular data provided by the National Oceanic and Atmospheric Agency (https://www.weather.gov/safety/heat‐index) and valid for T > 80^oF (27°C) and R > 40% (Weather.gov, 2019):

      Here, the units of the HI are ^oF. In California, relative humidity in the hot seasons rarely exceeds 40%. In these cases, we use the following “unofficial” biquadratic formula as a fit to the NOAA tabular values (Anderson et al., 2013):

As an example of the relationship of temperature to relative humidity, Figure 2.1 shows a scatterplot of temperature and relative humidity at the hottest time of day in Fresno. Colors are coded according to the NOAA heat index categories of risk to human health (Weather Service, 2020). Here and throughout this chapter, the heat index is calculated using weather station data in the HadISD data base (Dunn et al., 2016). We categorized the heat wave severity of the agricultural season by counting the number of days at each station when HI exceeds selected thresholds of 90^oF, 95^oF, and 100^oF. We then averaged these counts for all stations within a county. Furthermore, as specified in the empirical section, we tailored the estimated HI for specific crops during harvesting season for each of the counties under analysis.

Figure 2.1 Scatterplot of temperature and relative humidity at the Fresno, California airport weather station (HadISD station #723890 93193) at the time of the daily maximum heat index on hot days. Colors indicate the NOAA heat index categories.

2.3. HEAT WAVES AND AGRICULTURAL LABOR

      In the complex production and employment context of California agriculture, climate changes such as an increase in the frequency and intensity of heat waves may have significant consequences. The impact of heat waves on human health has been widely researched in both the rural and urban contexts (Guirguis et al., 2014; Luber & McGeehin, 2008; Sahu et al., 2013; Varghese et al., 2018). Research results show that heat exposure can have significant health effects, which include heat cramps, heat stroke, heat syncope, and death. The health effects of extreme heat are both short and long term (Basu, 2015). For example, in the urban context, an analysis of survivors of heat stroke for the 2003 heat waves in France showed that victims suffered a dramatic reduction in functionality and increases in early mortality rates (Luber & McGeehin, 2008). Furthermore, the socioeconomic cost of heat waves can be significant, because research shows that the number of hospital visits during heat waves exceeds the average number of hospital visits during non‐heat wave occurrences (Guirguis et al., 2014; Luber & McGeehin, 2008; Semenza et al., 1996). The increase in hospital visits associated with heat waves also result in higher financial costs associated with emergency room (ER) visits, as well as an increase in the social costs of heat waves via mortality impacts on the elderly, the very young, and the very ill (Bell et al., 2008; Guirguis et al., 2014). Despite the development and implementation of early warning systems, the negative impact of extreme heat in both the rural and the urban sector СКАЧАТЬ