Essays On Impacts Of Climate Change On Agricultural Sector In The Us

This dissertation investigates: (1) the climate change effects on the mean and higher order moments of crop yield distributions; (2) the effects of irrigation with and without its interactive terms with climate variables; (3) the climate effects on crop mix and climate change adaptation. The first essay explores how the climate change impacts the crop yield distribution. Using the flexible moment based approach, this study infers that external climate factors influence not only mean crop yield and variability, but also its higher order moments, skewness and kurtosis. The climate effects on each moment vary by crops. The second essay examines the irrigation effects on the mean crop yield. While the irrigation effects estimated from the model with irrigation dummy are constant regardless of climate conditions, the irrigation effects estimated from the model with irrigation dummy and interactive variables between irrigation and climate are affected by external climate factors. This study shows that as temperature increases, the irrigation effects are decreased and irrigation reduces damages from extreme temperature conditions. Precipitation and PDSI effects are also diminished under irrigation. The third essay explores the effects of climate on crop producers' choice. Our findings point out that the climate factors have significant impacts on crop choice and future climate change will alter the crop mix. Under the projected climate change of increasing temperature and precipitation, wheat and soybeans cropland will be switched to upland cotton. The major producing locations of upland cotton, rice, and soybeans will be shifted to the north. However, most of corn will be still cultivated in the Corn Belt and changes in acreage planted will not be significant.

This dissertation incorporates three independent essays on the impact of climate change on the United States' agriculture, with each explores a different facet of climate change. There have been heated debates about the potential impact of climate change on the United States' agriculture. Several influential studies such as Schlenker, Hanemann, and Fisher (2005, 2006), Schlenker and Roberts (2006) suggest a potentially large negative impact of climate change on farmland values and crop yields, while others including Mendelsohn, Nordhaus, and Shaw (1994), and Deschenes and Greenstone (2007) believe that there is little impact or the US agriculture could be a major beneficiary of global warming. These opposing results inspired my work to examine another aspect of climate change that has not been carefully addressed in the current literature: the impact of climate and weather extremes. While any individual extreme event cannot be causally linked to climate change, there could be a higher probability of more severe extreme events in the future. There are several potential scenarios in which we may expect more heating, less cooling, and less fluctuations between the extremes with different forms of distributional shifts in climatic conditions, all having the same change in the mean temperature. For example, climate change may result in increased precipitations in Northern America in the form of more droughts and more flooding events. These differential changes in the distribution of climatic conditions may have a subtle impact on agriculture, which could not be identified by studying moment variables such as the mean and the variance of temperatures or precipitations. The three essays inherited two major empirical methods widely used in estimating the impact of climate change: hedonic regression and panel data. Hedonic regressions (also called the Ricardian approach) utilize cross-sectional variations to identify how climatic conditions such as the average temperature or precipitation capitalize in farmland values, and panel estimations that employ within variations to link weathers with annual crop yields or farm profits. However, there is a situation in which both techniques are insufficient. If economic agents have forward-looking behaviors, and under uncertainties, the decision making process will involve a dynamic optimization problem whose a reduced-form approach as derived from either cross-sectional or panel data technique may not truly identify the actual behaviors. I devised an innovative dynamic programming approach built up on the Ricardian method to estimate the impact of natural disasters such as extreme drought events on cropland conversions. In the first essay, using historical crop yield reports paired with high-resolution climate data, I discovered a small and positive effect of a decreasing diurnal temperature range on yields of five major crops including corns, wheat, cotton, soybeans, and sorghum. The asymmetric increases in observed maximum and minimum temperature have resulted in a falling diurnal temperature range across the United States. This effect could help mitigate some potential harmful impacts of climate change in the future, averaging up to a two percent yield offset for summer crops. Meanwhile, little impact on winter crops is expected. Moreover, the overall impact of climate change from a rising mean temperature and less fluctuations is dominantly harmful for most crops. The second essay presents a structural model of cropland conversions with an application to the impact of extreme droughts. Droughts are perhaps the most destructive events to the US agriculture. Extended periods of severe droughts in the late 20th century caused widespread economic damages comparable to that of the Dust Bowl in 1930s. I showed that those events contributed to converting lands from agricultural production to urban uses by damaging soil productivity and lowering farming profits. I concluded the Ricardian approach to estimating climate change impacts is insufficient. Specifically, the Ricardian method works well for equilibrium adjustments by assuming that farm owners are able to make complete adaptations to a changing environment. However, the Ricardian approach fails to take into account the presence of climate extremes whose adaptations are neither possible nor costless. As a consequence, this method may underestimate the true cost of transient events related to climate change such as extreme droughts. This finding carries a significant implication for the future of the US' private croplands. As the US is predicted to experience more precipitations in the future with climate change, it seems that there would be a beneficial impact of more water for crops. It may not necessarily be the case, however. Even with increased precipitations, drought conditions may occur more frequently and intensively. Damages from potentially extreme drought events were not considered in the Ricardian estimates. In the third essay, I examined the impact of extreme heating conditions on prime farmland conversions in California using the hedonic regression technique with a spatial dataset. I focused on the number of extreme heating days, defined as day with the recorded maximum temperature rises above 90 degree Fahrenheit. I found a small but significant nonlinear impact of extreme heating days on farmland conversions. A mild increase in the number of extreme heating days may be good for crops, thus helps keep farmlands in agricultural production. However, too excessive heating is harmful and accelerates conversions out of farming.

This dissertation investigates: (1) the implications of including high-yielding energy sorghum under the Renewable Fuels Standard (RFS2) program; (2) the effects of RFS2 with and without projected climate change scenarios on U.S. agriculture; (3) the spatial distribution of cattle breeders in Texas to quantify how climate factors influence cattle breed selection. In the RFS2 energy sorghum work, the ability of the agriculture sector to meet the fuel requirements of RFS2 is examined with and without energy sorghum being a possibility using an agricultural sector model. The results show that energy sorghum would be a valuable contributor that would be used as a feedstock producing over 13 billion gallons per year of cellulosic ethanol. Without the presence of energy sorghum it is found that switchgrass serves as the major cellulosic ethanol feedstock. Findings also indicate that the presence of high-yielding energy sorghum does relax commodity prices and export reductions except for grain sorghum as energy sorghum competes with grain sorghum production. In addition, the results show that the introduction of energy sorghum has minimal effects on GHG mitigation potential in the agricultural sector. In the RFS2 and climate change research, the analysis shows that climate change eases the burden of meeting the RFS2 mandates increasing consumer welfare while decreasing producer welfare. The results also show that climate change encourages a more diversified use of biofuel feedstocks for cellulosic ethanol production, in particular crop residues. In the cattle breed research, summer heat stress is found to be a significant factor for breed selection: positive for Bos indicus and negative for Bos taurus and composite breeds. The estimation results also indicate a price-driven trade-off between Bos taurus and Bos indicus breeds.

This dissertation analyzes the impact of climate, and atmospheric carbon dioxide (CO2) on crop yields and grain transportation. The analysis of crop yields endeavors to advance the literature by statistically estimating the effects of atmospheric carbon dioxide (CO2) on observed crop yields. This is done using an econometric model estimated over pooled historical data for 1950-2009 and data from the free air CO2 enrichment experiments. The main findings are: 1) yields of soybeans, cotton, and wheat directly respond to the elevated CO2, while yields of corn and sorghum do not; 2) the effect of crop technological progress on mean yields is non-linear; 3) ignoring atmospheric CO2 in an econometric model of crop yield likely leads to overestimates of the pure effects of climate change and technological progress on crop yields; and 4) average climate conditions and climate variability contribute in a statistically significant way to average crop yields and their variability. To examine climate change impacts on grain transportation flows, this study employs two modeling systems, a U.S. agricultural sector model and an international grain transportation model, with linked inputs/outputs. The main findings are that under climate change: 1) the excess supply of corn and soybeans generally increases in Northern U.S. regions, while it declines in Central and Southern regions; 2) the Corn Belt, the largest producer of corn in the U.S., is anticipated to ship less corn; 3) the importance of lower Mississippi River ports, the largest current destination for U.S. grain exports, diminishes under the climate change cases, whereas the role of Pacific Northwest ports, Great Lakes ports, and Atlantic ports is projected to increase; 4) the demand for grain shipment via rail and truck rises, while demand for barge transport drops.

In this dissertation, I study the effects of climate change on agricultural total factor productivity and crop yields and their variability. In addition, an examination was conducted on the value of select climate change adaptation strategies in forestry. Across the study, the climate change scenarios analyzed were based on the 2007 Intergovernmental Panel on Climate Change Assessment Report. Climate change impacts on the returns to research investments were examined extending the work of Huffman and Evenson (2006), incorporating climatic effects. The conjecture is that the rate of return of agricultural research is falling due to altered resource allocations and unfavorable weather conditions, arising from the early onset of climate change. This work was done using a panel model of Agricultural Total Factor Productivity (TFP) for the forty-eight contiguous states over 1970?1999. Climatic variables such as temperature and amount and intensity of precipitation were added into the model. The main results are (1) climate change affects research productivity, varying by region; (2) this effect is generally negative; (3) additional investments are needed to achieve pre-climate change TFP rates of growth; and (4) the predicted investment increases are on the order of 18%. The second inquiry involved the impact of historical climatic conditions on the statistical distributions of crop yields through mean and variability. This was done statistically, using historical yields for several crops in the US, and climate variables, with annual observations from 1960 to 2007. The estimation shows that climate change is having an effect on the first two moments of the distribution, concluding that crop yield distributions are not stationary. The implication is that risk analysis must consider means and volatility measures that depend on future climatic conditions. The analysis shows that future mean yields will increase, but volatility will also be greater for the studied crops. These results have strong implication for future crop insurance decisions. Finally, an examination was done on the value of select forestry adaptation strategies in the face of climate change. This work is motivated by the known fact that forestry sector is already heavily adapted to changing climatic conditions. Using the Forestry and Agriculture Sector Optimization Model for the United States (FASOM), I found that rotation age is the most effective adaptation strategy being worth about 60 billion dollars, while changes in species and management intensity are worth about 1.5 billion, and land use change between forestry to agriculture is worth about 200,000.

This dissertation investigates three economic aspects of the climate change issue: optimal allocation of investment between adaptation and mitigation, impacts on a ground water dependent regional agricultural economy and effects on global food insecurity. This is done in three essays by applying mathematical programming. In the first essay, a modeling study is done on optimal temporal investment between climate change adaptation and mitigation considering their relative contributions to damage reduction and diversion of funds from consumption and other investments. To conduct this research, we extend the widely used Integrated Assessment Model?DICE (Dynamic Integrated Climate Economy) adding improved adaptation modeling. The model results suggest that the joint implementation of adaptation and mitigation is welfare improving with a greater immediate role for adaptation. In the second essay, the research focuses on the ground water dependent agricultural economy in the Texas High Plains Region. A regionally detailed dynamic land allocation model is developed and applied for studying interrelationships between limited natural resources (e.g. land and groundwater), climate change, bioenergy demands and agricultural production. We find out that the effect varies regionally across hydrologically heterogeneous regions. Also, water availability has a substantial impact on feedstock mix. In terms of biofuel feedstock production, the model results show that limited water resource cannot sustain expanded corn-based ethanol production in the future. In the third essay, a Computable General Equilibrium (CGE) model is applied in an attempt to study potential impacts of climate change on global food insecurity. Our results show that climate change alters the number of food insecure people in a regionally different fashion over time. In general, the largest increase of additional food insecure population relative to the reference case (no climate change) is found in Africa and South Asia, while most of developed countries will benefit from climate change with a reduced proportion of food insecure population. In general, climate change affects world agricultural production and food security. Integrated adaptation and mitigation strategy is more effective in reducing climate change damages. However, there are synergies/trade-offs between these two options, particularly in regions with limited natural resources.

Agriculture is highly dependent on and sensitive to weather. Warming effects result from greenhouse gas emissions and aerosols from a small number of countries but its impact will be felt on a global scale. So far, agricultural productivity growth has sustained the continuous global supply of food but will this continue into the foreseeable future with the incidence of climate change? The effects of climate change on crop yields have been the focus of several studies. However, the sensitivity of agricultural productivity (measured as Total Factor Productivity-TFP) to climate change is not well understood. The first essay examines how historical changes in temperature and precipitation have affected the evolution of agricultural total factor productivity (TFP) while accounting for the short and long term impact. A fixed effect regression model for 128 countries for a period of 1961 to 2014 was employed to exploit yearly changes in temperature and precipitation as the identification strategy. Results show that precipitation has a significant effect on TFP growth in Sub-Saharan Africa, tropical and low income countries. Global short term temperature effect is offset in the long run showing that farmers adapt to reduce the effects of temperature in their behavioral decisions. Irrespective of the impact of climate change, there have been calls for an increase in agricultural productivity due to uncertainty and a global decline in Research and Development (R&D) expenditures. Previous literature accounts for the effect of global TFP growth on global food security and the environment. My second essay estimates the impact of TFP growth in different regions on global food security and the environment using a partial equilibrium model. To construct comparable TFP shocks across regions, I consider three TFP shock scenarios: (i) a uniform 100 percent increase in TFP growth in each region, (ii) TFP growth in each region that gives the same decrease in global commodity price, and (iii) TFP growth in each region resulting from the same increase in R&D expenditure. Results show that a 100% increase in TFP in the US & Canada increases agricultural carbon emission within the US & Canada by 16.9% but with a net global decrease in agricultural carbon emissions by 4.27%. In addition, a 100% increase in TFP in the US & Canada decreases global food security (malnutrition) by 13.09%. These results provide justification to support increasing R&D expenditures in developed regions. Overall, TFP growth is most effective in Sub-Saharan Africa as it gives the largest reductions in malnutrition and carbon emissions.

This dissertation is composed of three essays, investigating two aspects of the role of agricultural sector in climate change mitigation: leakage and additionality. Leakage happens when mitigation policies reduce net GHG emissions in one context, but increase (decrease) prices, which in turn causes production (demand) expansion resulting in an offsetting rise in emissions elsewhere. The first essay documents an integration of a US domestic agricultural sectoral model and a global agricultural sectoral model, with the aim to deliver better leakage assessment. The second essay investigates the trend of US crop yield growth and its implication on the international leakage effect. We find that the slowdowns have occurred to the growth rates of most US major crops. The implementation of climate change mitigation strategies, such as the expansion of bioenergy production, causes demand for the agricultural sector to increase substantially. The new demand would cause noticeable leakage effect if crop yields continue to grow at the current rates. Such effect may be potentially alleviated by higher crop yield growth rates; but the extent of alleviation depends on the mix of technological progress obtained across crops as well. Additionality is often a concern in programs designed to incentivize the production of environmental services. Additionality is satisfied if payments are made to services that would not have occurred without the payment. However, because of the information asymmetry between service buyers and sellers, ensuring additionality poses a challenge to program designers. The third essay investigates how the pursuit of ensuring additionality would complicate environmental policy design with a theoretical model. Specifically, we examine 4 types of policy design, including 2 discriminating schemes and 2 simpler non-discriminating schemes. We found that under certain conditions, some of the non-discriminating schemes can be almost as good as the discriminating ones. Findings in this dissertation contribute to inform policy makers about the potential impacts of climate change mitigation policies in the agricultural sector and also help to improve understanding of environmental program design.

Essay from the year 2015 in the subject Politics - Environmental Policy, grade: 2,0, University of Paderborn, language: English, abstract: The following essay deals with agriculture as a polluter and contributor to climate change in the United States. I have chosen this topic because it currently has a high degree of relevance in the world, and I am very interested in this topic. I have also chosen this theme because I like to analyse current issues to determine the problems of the topic, and to calculate the impacts and to potentially find ways to solve problems. I think a good analysis of this topic needs to be made. In recent decades’ attention has increasingly been drawn to global climate change because of the belief that changes are happening to the earth. Today the visible consequences seem like they are increasingly occurring. Examples of this are severe natural disasters, global warming and this in turn causes increased social disparities, especially so in developing countries. Practices related to agriculture are a main polluter and therefore a contributor to climate change and a climate, especially in the United States. I will show this with my essay. For this I explain the reasons, problems, impacts and solutions. In addition, I'll show the view of the U.S. Government and what people can do about this problem.