The significant vgsc mutations observed could be a result of selection pressure build-up that is due to more contact with insecticides in indoor-based interventions. From Kisian, the G119S mutation was present at low frequencies even though it was higher in the progeny of mosquitoes resting indoors compared to those resting outdoors. This was more in Kisian, where the vgsc mutations were at lower frequencies than in Kimaeti. These findings suggest that these mutations could be arising from different pressures that could be present in the lowland and absent in the highland.The metabolic enzymes, associated with insecticide resistance activities were found to be elevated, more in indoor resting malaria mosquitoes compared to the outdoor counterparts from both sites. From the phenotypic assays, pre-exposure to PBO synergist restored the susceptibility of the malaria vectors to the pyrethroids commonly used in LLINs by public health. Phenotypic exposures with prior PBO contact demonstrated more activity of monooxygenases in aiding metabolic resistance. The involvement of monooxygenases in pyrethroid resistance has been reported in Western Kenya. In Kimaeti, there was increased levels β-esterases, higher indoors than outdoors. Kisian, on the other hand, did not show involvement of β-esterases in contributing to resistance as shown by similar levels in indoor and outdoor resting mosquitoes. The glutathione-S-transferase possibly played a part in the resistance levels as a previous study reported since it was higher in mosquitoes resting indoors than those resting outdoors from both Kisian and Kimaeti. These levels, therefore, suggest that monooxygenases were the main mechanism of insecticide resistance in Kisian, especially with the low frequency of resistant alleles, whereas in Kimaeti, the case pointed be a combination of genotypic and metabolic mechanisms. The expression of phenotypic,grow solutions greenhouse genotypic and metabolic resistance appears to be higher in indoor than outdoor resting malaria mosquitoes in these regions.
The widespread use of LLINs in attempts to controlling these vectors and the extensive agrochemical use could be strengthening the increase of insecticide resistance in the sites. The higher levels indoors suggest that these mosquitoes could be resting indoors because they are adequately resistant to the insecticides used in LLINs, posing a threat to the wide coverage LLINs. On the other hand, outdoors, the resistance mechanisms were present as well pointing to exposure to these insecticide-based interventions in just enough pressure to elicit expression of the resistance traits. The levels of resistance could be enough to elicit an increase in malaria incidence due to the reduced mortality of resistant malaria vectors that could hinder current vector control interventions.Increasing temperatures and higher variability in precipitation in California are part of a larger regional trend in the Western United States . This is consistent with global trends that indicate that 2000-2010 has been warmer at the Earth’s surface than any preceding decade since 1850 . Observed increases in temperature and precipitation extremes in semi-arid regions, such as Southern California, clearly translate into more severe future impacts than analogous trends in temperate regions, such as projections of increased frequency and duration of heat waves and droughts over the remainder of the current century . Previous studies suggest that agriculture in the largely irrigated Western United States may not be as susceptible to precipitation trends as agriculture in the more temperate East . This holds for long-run mean precipitation conditions . However, this conclusion minimizes the severity of the recent drought experienced in California with historically low precipitation and soil moisture levels . The recurrence and longer duration of droughts in California over the past two decades has greatly affected the agricultural industry, which, on average, uses about 80% of freshwater resources . Figure 1.1 illustrates the percentage of California’s area in drought from 2000-2016. Not only does this reveal the large spatial and temporal extent of the most recent drought, but the colors reveal the large area under extreme and exceptional drought from mid-2013 to 2017. The most immediate economic impacts are lost agricultural revenue emanating from fallowed acres and yield declines, and farm job losses for one of the most vulnerable socioeconomic groups. For example, the 2009 drought resulted in revenue losses of $370 million with fallowing of 285 thousand acres in the San Joaquin Valley, and almost 10 thousand farm jobs losses .
Arguably the most important variables explaining how agriculture will be affected by climatic changes are those of human ingenuity at the farm level. Human ingenuity is simply another word for adaptation to climate change in order to minimize welfare losses. Thus, the overarching theme of our three subsequent analyses is quantifying grower responsiveness to farm-level microclimate in Southern California, our study area. Using original survey data, we study differential impacts of short-run weather and long-run climate—based on farm size, type, and water source—on productivity per acre and likelihood of adopting water management practices, which have not been studied in previous county-level analyses. Further, we are able to decompose water sources into price, pricing structure, frequency of rate increases, senior water rights, quality, and type of source . In addition to studying farm-level productivity, we study short-run fluctuations in weather on likelihood of adoption of water management technologies and practices, and on parcel-level land sales. Our contribution to the literature is based upon an original survey instrument we developed and disseminated to growers in the region . The contact information was taken from the respective county Agricultural Commissioner Offices. This survey is comprised of 28 multiple choice and fill-in questions on grower, farm, and water source characteristics. This was disseminated via mail by a team of 3 undergraduate students, to growers in the study region, with a 14.6% response rate. We focus on Southern California agriculture, specifically Imperial, Riverside, San Diego, and Ventura counties. The region is often overlooked as analyses tend to focus on the Central Valley, California’s most productive agricultural region. Yet, there are several crops for which 50% or more of California’s production originates in these four counties, including raspberries, lemons, flowers and foliage, avocado, and sudan hay. All of the state’s date and sugar beet production originates in these four counties . Imperial, Riverside, San Diego, and Ventura counties are amongst the top 15 agricultural counties in the state, representing approximately 16% of statewide agricultural revenue . They also represent the diverse climate of the region with two coastal , and two desert counties.
The 4 counties also vary in farm size with San Diego County having the largest share of farms under 10 acres, and, at the other extreme, Imperial County having the largest share of farms with 1000 or more acres . There is also a wide distribution in gross revenue across these counties . An immediate concern with aggregation at the county level is the omission of data on decision-maker/grower , farm ,marijuana drying rack and detailed water source attributes . Excluding such information assumes a priori a limited role of the economic agent to influence farmland productivity. It also simplifies the inherent complexity in representing farm and water source characteristics. It is not for lack of explanatory power that these variables are excluded. It is more likely that they would have been studied had they been available in existing data sources. The USDA Farm and Ranch Irrigation Survey , a major source of US agricultural data for economic analyses, does not provide these variables at the farm level to researchers. There is, however, little reason to assume that the climate, soil, and water variables in county-level studies are correlated with any of these microlevel variables, thus ruling out the potential bias in climate, soil, and water estimators. Aggregation at the county level also leaves the model susceptible to measurement error on certain explanatory variables . Measurement error is defined as an imprecise measure of an economic variable, dependent or explanatory, which has a well-defined quantitative meaning . 1 Following the classical errors-in variable assumption, this could lead to estimators that are asymptotically inconsistent and biased downward in their respective probability limits . 2 The remaining sections in this chapter present the theoretical framework behind each of the 3 empirical analyses in this dissertation: the Farm-Level Ricardian, the Discrete Choice of Adoption, and the Parcel-Level Models. Each subsection also includes hypotheses on the impact of climate and other key variables on the respective dependent variables . In addition to studying the impact of climate and other relevant variables on farmland productivity, we study the factors influencing the adoption of technologies to monitor soil moisture and salinity.5 Adoption of climate-effective monitoring practices is particularly important as projections of prolonged drought continue throughout the current century. Most growers in our sample have already adopted micro-irrigation practices for vegetables, orchards, and vineyards, and extension experts suggest that consistent and/or sophisticated monitoring of growing conditions represents the next stage of irrigation efficiency adaptations .
Salinity monitoring affects water availability in both the short and long run. Too much leaching leads to water waste and, ultimately poor irrigation and economic efficiency. Too little leaching affects soil salinity and water quality at both the farm and basin level, and ultimately water availability at the farm-level in the long run. We implement logistic regression, consistent with previous studies on technology adoption , to study the factors influencing adoption of at least one soil moisture monitoring practice , or at least one water salinity monitoring practice . Prior to implementing the pilot survey, we received approval from the UCR Institutional Review Board.There were two primary objectives to the pilot survey: field-test survey questions, and gauge response rate. Rather than rely on focus groups to field-test the survey questions, we chose to disseminate a pilot survey. The major benefit of sending a pilot survey is that we could potentially receive valuable input from respondents who could not participate in focus groups due to financial, time, or physical constraints. A second benefit was time savings in survey implementation. Focus groups require managing multiple schedules to find a convenient meeting time and place, and possibly funding travel and accommodation. Although we planned to disseminate an online survey, we had not yet at that stage secured assistance from either Agricultural Extension or Farm Bureaus in each county to host our survey. In order to save time, we sent the pilot survey via postal mail using contact information from the Agricultural Commissioner Pesticide Permit Database . An informal team of fellow graduate students and family/friends helped prepare the pilot phase mailings. Each mailing package included invitation letters , consent documents , first version of questionnaire , and a self-addressed return envelope. Using a random-number function in Microsoft Excel, we randomly selected 300 respondents in total from Riverside and San Diego counties. We selected these counties as they are representative of the type of agriculture found in the region . Based on our discussions with extension experts,8 we were sensitive to the potential apprehension with which Imperial County growers, in particular, would react to our survey. Growers in Imperial County have held senior water rights for over a century due to the Seven-Party Agreement.They are aware that they have been criticized for using less efficient irrigation practices , and many fear that they will be mandated to change these practices . Thus, they may be hesitant to providing any information on irrigation and other practices. In order to minimize Imperial growers’ time burden, we chose to field test the survey on a potentially more receptive audience, and send only the final survey to Imperial. Since Ventura County has a relatively similar distribution of farm types as San Diego County , we also decided to exclude Ventura from the pilot. The pilot survey consists of 20 questions, including grower characteristics , farm characteristics , water source characteristics , water management practices , perceptions of water scarcity , and an open-ended comment space at the end of the survey . The majority of these questions are multiple choice often with an “other” choice that included an option to write in a response that was not pre-determined. Eight questions are fill-in style. We received a roughly 10% response rate from the pilot phase , and learned valuable lessons on question structure for preparing the final survey. First, there were far too many questions on water scarcity perceptions, which could be consolidated into fewer questions. Second, income questions were better placed at the end of the survey to minimize participant suspicion.