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Abstract

Over the past 20 years, U.S. agriculture has witnessed profound changes with respect to technology, climate, farm policy, and other factors (ethanol production, Chinese demand, etc.) that have major repercussions with regards to the geographic distribution of crop production — particularly, from a market share and geographic basis. There have been many recent studies that have examined both the direct and indirect impacts of these production factors upon crop yields, acreage, and production from both a temporal and spatial perspective. However, little to no attention has been paid to the impact of these factors upon the relative influence of each individual state’s crop production outcomes as they relate to the national outcome. The purpose of this study is to address this question of state-level geographic importance for U.S. corn and soybeans by employing the following procedure. First, a metric is constructed to measure crop production outcomes at any geographic level by comparing the current year’s production to the recent historical norm. This metric, called a production performance index (PPI), is simply the difference between the current year’s crop production and the Olympic average (drop minimum and maximum and take arithmetic average of remaining values) of the previous five years of production. The dataset used in the study includes annual crop production values for the 1970 through the 2017 crop years. The PPI, given its five-year lag, is calculated with values for the U.S., each major producing state, and the “Other States” residual from the 1975 to 2017 crop years for both corn and soybeans. The PPI time series is divided into two distinct sets of time periods as a proxy for the changes mentioned above: (1) the 1975 to 1995 crop years, and (2) the 1996 to 2017 crop years. The 1996 crop year was chosen as the dividing point since it represents a watershed year in U.S. corn and soybean production — the commercialization of the first GMO corn (Bt corn) and soybean (Roundup Ready) varieties. Each states’ relative influence upon the national production performance outcome is determined by regressing the individual states’ PPI values upon the national PPI value for corn and soybeans under each time period. The regression analysis is conducted using correlated component regression (CCR) – a relatively new statistical tool for sparse and mulicollinear datasets. The absolute value of the standardized coefficient values from the regression model are used to rank each state with regards to its influence. Each state’s percentage share of the sum of the absolute coefficient values was also calculated and used to calculate a Herfindahl-Hirschman Index (HHI) by summing the squared values of the percentage shares. The HHI is used as a measure of the geographic dispersion of production importance for the national aggregate. Overall, the results showed a shifting geographic dynamic for both corn and soybeans with the emphasis shifting from east to west in general direction. This makes intuitive sense as many of the observed technological and climatic changes over the past several decades point towards corn and soybean varieties that require a shorter growing season, and the increase in the number of frost-free days in many of the states in the northern reaches of the U.S. Corn Belt region. Additionally, the greater utilization of irrigation in crop production has likely contributed to the westward expansion of both corn and soybean production — often at the expense of wheat and cotton production. The slight decline in the HHI for corn indicates that production influence is becoming slightly more diversified from a geographic perspective. For soybeans, the opposite effect has occurred with a slight increase in the HHI pointing towards greater influence from the key producing states of Iowa, Minnesota, and Illinois — likely the result of a shift from corn to soybean acres as all three states lost influence shares in corn production between the two time periods.

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