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Estimates of crop supply response to price have a long history in agricultural economics. The primary contribution of this dissertation is to estimate the short-run (1-year) and long-run (5- to 10-year) acreage response to price for corn and soybeans. I characterize the dynamics of aggregate supply response by aggregating a field-level conceptual model of crop decisions with rotation incentives across heterogeneous fields. The model indicates that the long-run response is likely to be smaller than the short-run response. The econometric model exploits crop data, in the form of pixels, derived from satellite imagery for Illinois (1999-2010) and Iowa and Indiana (2000-2010). To approximate fields as the unit of analysis, I use Common Land Unit boundaries from the Farm Service Agency. Expected per bushel crop revenues are the sum of a futures price, an expected loan deficiency payment, and an expected basis obtained from 93 market locations. Soil and precipitation data are spatially merged to the crop data. The field-level dataset contains approximately 1 million observations per year. I argue that previous estimates of aggregate acreage response to price have been biased due to the use of aggregate data and pooled estimators (i.e., estimating a heterogeneous coefficient with a single parameter). The bias from aggregation occurs because aggregating a dynamic model results in a different dynamic structure at the aggregate level, thus the model is misspecified with a single lagged dependent variable. The pooling bias occurs because the coefficient on the lagged dependent variable captures the variation in the price response. When the price of corn increases, fields that have a larger price response are more likely to plant corn in the current year, but they are also more likely to have planted corn the previous year since prices are positively autocorrelated. I avoid these sources of bias by using disaggregate crop data and estimating coefficients that differ by soil regimes. Using the field-level data, I estimate the own-price acreage elasticity for corn as 0.52 in the short run and 0.34 in the long run. I estimate the own-price acreage elasticity for soybeans as 0.31 in the short run and 0.23 in the long run. In contrast, estimates with county-level data for the same region and time period indicate that the long-run elasticities are larger than the short-run elasticities.
Understanding how producers make decisions to allot acreage among crops and how decisions about land use are affected by changes in prices and their volatility is fundamental for predicting the supply of staple crops and, hence, assessing the global food supply situation. This study makes estimations of monthly (i.e., seasonal) versus annual global acreage response models for the world's principal staple food crops: wheat, corn, soybeans, and rice. Primary emphasis is given to the magnitude and speed of the allocation process. Estimation of intra-annual acreage elasticity is crucial for expected food supply and for input demand, especially in the light of the recent short-term volatility in food prices. The econometric results indicate that global crop acreage responds to crop prices and price risks, input costs as well as a time trend. Depending on respective crop, short-run elasticities are about 0.05 to 0.40; price volatility tends to reduce acreage for some of the crops; comparison of the annual and the monthly acreage response elasticities suggests that acreage adjusts seasonally around the globe to new information and expectations. Given the seasonality of agriculture, time is of an essence for acreage response. The analysis indicates that acreage allocation is more sensitive to prices in the northern hemisphere spring than in winter and the response varies across months.
Crop rotation systems are an important part of agricultural production for managing pests, diseases, and soil fertility. Rotation system decisions are based on a tradeoff between the immediate profits from a crop planted this season with the future costs (or benefits) manifest through changes in yield and production costs for future crops. Field-specific capital such as soil characteristics, micro-climate, water quality, and farmer management skills alter the inter-temporal rotation effects between crops. In other words, agricultural production decisions are dynamic processes which will vary across regions with heterogeneous field-capital. Due to a dearth of field-level data, agricultural production is rarely modeled as the solution to a dynamic economic problem. Similarly, much of the existing literature focuses on aggregate regional models which omit spatial variation in production conditions across regions. The dynamics and spatial variation of rotation system decisions have potentially important implications for spatially-dependent agricultural-environmental policies, sustainable agriculture, and accurate representation of the spatial and dynamic components of supply response. Historically, decisions at the field-level of detail have been difficult to observe consistently across time in anything other than experimental plots. In this dissertation I use a unique geo-referenced panel dataset of field-level production covering over 14,000 fields (over 1 million acres) and 13 years. I use these data to estimate empirically observed crop rotation systems using methods from the Bioinformatics literature. I estimate reduced form models of land use and rotation system choice at the field-level in order to determine what factors influence production decisions. Based on the insights of the reduced form analysis, I develop a theoretical model of the two-crop rotation problem and prove that the optimal solution is an infinite cycle of discrete switches between crops. I generalize the two crop theoretical model to a dynamic programming model with multiple crops and estimate the structural parameters which satisfy the Euler conditions for a one-year carry-over effect between crops. I solve the dynamic programming model and simulate supply response to spatial heterogeneity in field-specific physical capital and exogenous price shocks.
This article estimates a worldwide aggregate supply response for key agricultural commodities-- wheat, rice, corn, and soybeans--by employing a newly-developed multi-country, crop-calendar-specific, seasonally disaggregated model with price changes and price volatility applied accordingly. The findings reveal that, although higher output prices serve as an incentive to improve global crop supply as expected, output price volatility acts as a disincentive. Depending on the crop, the results show that own-price supply elasticities range from about 0.05 to 0.40. Output price volatility, however, has negative correlations with crop supply, implying that farmers shift land, other inputs, and yield-improving investments to crops with less volatile prices. Simulating the impact of price dynamics since 2006, we find that price risk has reduced the production response of wheat in particular--and to a lesser extent, rice--thus dampening price incentive effects. The simulation analysis shows that the increase in own-crop price volatility from 2006-2010 dampened yield by about 1-2% for the crops under consideration.
Agricultural trade and development is a backbone of international trade. It includes agricultural trade patterns, commercial policy, international institutions such as WTO, Tariff and non-tariff barriers in international trade, exchange rates, biotechnology and trade, agricultural labour mobility, land reform, environment and the areas and issues spanning these areas. This book brings together leading research and issues in this fundamental field.
People are constantly changing the land surface through construction, agriculture, energy production, and other activities. Changes both in how land is used by people (land use) and in the vegetation, rock, buildings, and other physical material that cover the Earth's surface (land cover) can be described and future land change can be projected using land-change models (LCMs). LCMs are a key means for understanding how humans are reshaping the Earth's surface in the past and present, for forecasting future landscape conditions, and for developing policies to manage our use of resources and the environment at scales ranging from an individual parcel of land in a city to vast expanses of forests around the world. Advancing Land Change Modeling: Opportunities and Research Requirements describes various LCM approaches, suggests guidance for their appropriate application, and makes recommendations to improve the integration of observation strategies into the models. This report provides a summary and evaluation of several modeling approaches, and their theoretical and empirical underpinnings, relative to complex land-change dynamics and processes, and identifies several opportunities for further advancing the science, data, and cyberinfrastructure involved in the LCM enterprise. Because of the numerous models available, the report focuses on describing the categories of approaches used along with selected examples, rather than providing a review of specific models. Additionally, because all modeling approaches have relative strengths and weaknesses, the report compares these relative to different purposes. Advancing Land Change Modeling's recommendations for assessment of future data and research needs will enable model outputs to better assist the science, policy, and decisionsupport communities.
How we produce and consume food has a bigger impact on Americans' well-being than any other human activity. The food industry is the largest sector of our economy; food touches everything from our health to the environment, climate change, economic inequality, and the federal budget. From the earliest developments of agriculture, a major goal has been to attain sufficient foods that provide the energy and the nutrients needed for a healthy, active life. Over time, food production, processing, marketing, and consumption have evolved and become highly complex. The challenges of improving the food system in the 21st century will require systemic approaches that take full account of social, economic, ecological, and evolutionary factors. Policy or business interventions involving a segment of the food system often have consequences beyond the original issue the intervention was meant to address. A Framework for Assessing Effects of the Food System develops an analytical framework for assessing effects associated with the ways in which food is grown, processed, distributed, marketed, retailed, and consumed in the United States. The framework will allow users to recognize effects across the full food system, consider all domains and dimensions of effects, account for systems dynamics and complexities, and choose appropriate methods for analysis. This report provides example applications of the framework based on complex questions that are currently under debate: consumption of a healthy and safe diet, food security, animal welfare, and preserving the environment and its resources. A Framework for Assessing Effects of the Food System describes the U.S. food system and provides a brief history of its evolution into the current system. This report identifies some of the real and potential implications of the current system in terms of its health, environmental, and socioeconomic effects along with a sense for the complexities of the system, potential metrics, and some of the data needs that are required to assess the effects. The overview of the food system and the framework described in this report will be an essential resource for decision makers, researchers, and others to examine the possible impacts of alternative policies or agricultural or food processing practices.
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Aimed primarily at advanced graduate students and professional biologists, this book explores the degree to which animal*b1plant interactions are determined by plant and animal variability. Many of the patterns seen in natural communities appear to result from cascading effects up as well as down the trophic system. Variability among primary producers can influence animal and plant population quality and dynamics, community structure, and the evolution of animal*b1plant interations.