With respect to sociodemographic factors, mothers that are exposed to extreme levels of pesticide are more likely to be minorities and have lower education than the sample population as a whole. While we control for these factors, there is potential for the high exposure sample to differ in other unobserved ways that could yield a higher likelihood of adverse birth outcomes. If so, this would result in overestimates of the effects of pesticide exposure on adverse birth outcomes. Additionally, we measure pesticide exposure as all pesticide use on production agriculture in the 2.6 km2 PLS Section encompassing mothers’ addresses. We do so because the diversity of chemicals applied in the San Joaquin Valley is extensive and the cumulative effects of multiple exposures are not well understood. However, some chemicals or combinations of chemicals may not be relevant to reproductive risk. Thus, our coefficients are likely underestimates for individuals exposed to a disproportionately high fraction of chemicals of reproductive concern for their PLS Section, year and birth month. There is some indication that closer proximity to agricultural fields results in increased odds of adverse birth outcomes. For a study of this spatial and temporal breadth it is infeasible to directly measure distance from a sprayed field. However, for the San Joaquin Valley, PLS Sections that have any agriculture generally are agriculturally dominated. Furthermore, the PLS Section is roughly 2276 m on a diagonal. Thus it is highly likely that the vast majority of households in PLS Sections with pesticide use are within 1000 m of a treated agricultural field. If pesticides dissipate much more rapidly,cut flower bucket such that the effect is concentrated within 100 m of pesticide use, our study design would underestimate this relationship due to dilution with individuals living farther away from fields but still within the same PLS Section exposure.
However, for this to be occurring, the population residing on-farm or adjacent to fields must be much smaller than the broader population residing in the San Joaquin Valley for us to observe such small coefficients. Indeed, this makes intuitive sense for California, where farm workers overwhelmingly report living independent of their employers in houses or rental units, particularly if they have a spouse or children. However, our results may under predict adverse birth outcomes in regions where a larger proportion of workers reside in employer-provided housing on or adjacent to fields, where a larger fraction of pesticide are applied aerially, or where permissible chemicals are more environmentally persistent or toxic to humans. We also lack information on residence time at mother’s address and employment. Much of the San Joaquin Valley economy is driven by the agricultural industry. If farm workers were mostly migratory and followed the harvest, our measure of residential pesticide exposure would be inaccurate for this subset of the population. Yet, according to the National Agricultural Workers Surveys for 1996–2011, California farm workers, especially if they have a spouse or children in their household, are settled. Our measure of exposure would also be artificially high if women were applying agricultural pesticides during pregnancy. While ~18% of California farm workers are women, only 1.5% of women reported using pesticides in the past 12 months and 0% of women with a spouse or children had reported doing so. Women could get additional exposure via their spouses, and ~16% of male farm workers reported loading, mixing or applying pesticides in the past year. Finally, the San Joaquin Valley is well known to have substandard environmental quality, frequently exceeding EPA contamination levels for air quality. If such exposures co-vary with pesticide use and vary at small spatial and temporal scales, the coefficient on pesticide exposure could capture additional contamination despite our PLS Township-year and birth month controls. While we cannot be certain we have eliminated all sources of contamination that co-vary with pesticides, including a rich set of ambient air quality and temperature metrics did not change the basic results of this paper. In conclusion, there is a growing literature on the relationship between pesticide exposure and adverse birth outcomes. Yet, evidence of a causal link between infant health and agricultural pesticide exposure remains uncertain due to small samples and lack of maternal or birth characteristics.
Our study is the most comprehensive to date, bringing together the largest data file ever compiled on street-address level birth outcomes and fine scale exposure to agricultural pesticides. We provide robust evidence that there are multiple negative effects of residential agricultural pesticide exposure on adverse birth outcomes, but only for births exposed to very high levels of pesticides during gestation. The documented concentration of impacts in the extreme upper tail of the pesticide exposure distribution may explain why previous studies fail to consistently detect effects of pesticides on birth outcomes. Furthermore, the concentration of impacts in the extreme tail of the pesticide exposure distribution provides policy challenges and public health opportunities to balance these potentially serious but rare outcomes with the societal benefits of continued pesticide use.Although the response of agricultural systems to climate is drawing considerable attention because of the potential for a global food crisis, current understanding of how climate affect agricultural production is highly uncertain since the feed backs between them are not well studied. Agricultural systems are highly vulnerable to climate variability, where the area suitable for agriculture, the length of growing seasons and yield potentials are expected to change under warming scenarios [IPCC, 2007]. In addition, crop growth alters some important physical climate forcings, such as latent heat flux, shortwave radiation, long wave radiation and soil moisture. This two-way interaction is often referred to as a feedback, describing a nonlinear cycle between two systems. Clarifying the importance of these feed backs could improve regional climate simulations in agriculturally intensive areas and enable better prediction of crop production. Variability in atmospheric CO2, temperature and precipitation highly affect agricultural production. The elevated CO2 could increase photosynthetic productivity [Aoki and Yabuki, 1977; Cooper and Brun, 1967; Moss, 1962] and therefore lead to an increase of yield. Amthor [2003] reviewed the previous observations and suggested doubling CO2 could increase the yield by 31% in average. At the same time, double CO2 could lead to 34% reduction of transpiration and double water use efficiency [Kimball and Idso, 1983]. In one study, increase in variability of temperature and precipitation resulted in significant increases in yield variability and crop failures [Mearns et al., 1992]. Warming by 2-4 o C could results in substantial shortening of the growing season, and change of crop calendar, particularly in winter [Butterfield and Morison, 1992]. Furthermore, increasing temperature and precipitation could have different impacts on yields for different crops. For example, a simulation study indicated potato production was increasing while wheat and faba bean was decreasing with increased temperature, and increasing of precipitation had no effect on the yield of potatoes or spring wheat, but could reducing winter wheat yield [Peiris et al., 1996]. Meanwhile, agriculture also affects climate by altering the surface energy, water, and carbon cycle. Cropland plays a very important biogeophysical role in changing climate [Feddema et al., 2005; Foley et al., 2005; Pitman et al., 1999].
Agricultural expansion in business as usual scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans [Feddema et al., 2005]. Crops alter the small-scale boundary layer structure [Adegoke et al., 2007], such as surface wind and boundary layer height, by increasing canopy height during the growth process. Compared to natural vegetation,flower display buckets cropland has higher albedo that alters the energy budget when converting between forest and cropland [Bonan, 2008; Oleson et al., 2004]. Cropland also alters the water cycle. Both field observations and modeling have shown that conversion of forest to cropland can reduce evaportranspiration and precipitation at the regional scale [Sampaio et al., 2007]. Moreover, agriculture and associated management practices were found to affect the carbon cycle [Lal, 2004]. Global simulation indicates a 24% reduction in global vegetation carbon due to agriculture [Bondeau et al., 2007a]. Growing biofuel crops at previously natural vegetation land could increase greenhouse gas emissions by 50% [Searchinger et al., 2008]. Both observations and numerical modeling are used to study climate effects on agriculture. Laboratory studies using growth chambers and greenhouses showed elevated CO2 could increase net photosynthesis [Aoki and Yabuki, 1977; Cooper and Brun, 1967; Moss, 1962]. These stuides had a short period measurements and the high CO2 concentrations were not realistic. Free air CO2 enrichment experiments [Ainsworth and Long, 2005; Ainsworth et al., 2002; Long et al., 2006] using long term observation confirmed some chamber experiment results that trees were more responsive than herbaceous species to elevated CO2, but crop grain yields increased far less than in previous enclosed studies. Regression models [Rosenberg, 1982] also have been employed to study how climate affects crop yield and this method is still widely used today [Diffenbaugh et al., 2012; Lobell et al., 2008b]. Finally, crop growth models enable yield prediction and hazard prevention.Climate models were first developed for numerical weather prediction in the 1950s, and had a very coarse resolution only contained atmosphere circulation. In 1960-1970s, the climate model included both ocean and atmosphere circulations. In 1980-2000s, the development of regional climate model and sub-grid physical process model not only aim to improve the forecasting skill but also to study the climate change. In climate model, the land surface model provides sensible, latent, and momentum flux for atmosphere model to solve the atmospheric equations. The potential climate sensitivity to land use change is determined by the difference between two simulations that differ only in land use. A key determinant in accuracy of such research is how well the land surface model simulates the surface energy fluxes . The development of land surface model is getting more and more comprehensive to reflect the reality [Bonan, 2008]. Early land surface models represented the physical processes using simple parameterizations. For example, the soil hydrology was represented as a bucket, which could hold some maximum amount of water filled by precipitation, with the excess water becoming runoff.
Currently, most land surface models include all the major parameterizations, such as vegetation photosynthesis and conductance, snow accumulation and melting, radiation transfer, and turbulence processes above and within the canopy, etc. Moreover, some advanced land surface models include the carbon cycle and dynamic vegetation growth. Coupling a land surface model that incorporates dynamic crop growth into a climate model enables simulation of the two-way interactions between climate and crop growth. Recent work incorporating crop growth models into climate models has revealed that dynamic crop growth strongly influences regional climate patterns by altering land surface water and energy exchange. Most of these studies have not rigorously evaluated results against observations of climate and crop variables. Further, interactions between crop growth and irrigation effects on climate are not well examined. The aim of the work is to improve a regional climate model by incorporating a land surface model that simulates dynamic crop growth. Particularly, my work focuses on the improvement and evaluation of the Weather Research and Forecasting Model with updated Community Land Model , a dynamic crop growth model, and an irrigation scheme. As the next-generation mesoscale numerical model, the standard version of WRF includes relatively simple land surface schemes, which potentially constrain model applications for studying the land surface and ecosystem-atmosphere feed backs. By adding the CLM into WRF, I expected an improvement in surface energy flux simulations. Therefore, I first validated the performance for the surface energy fluxes for four vegetation types across the continental of United States in the first chapter [Lu and Kueppers, 2012]. Since one problem in this model was related to the low crop LAI bias and lack of irrigation, I further incorporated the dynamic crop growth model and irrigation into a new version . I evaluated the crop growth and climate variables in the new version and the influence of dynamic crop growth on irrigation effects was quantified. In the third chapter, I used the coupled model to study irrigation effects on heat wave frequency, duration, and intensity.