Agricultural issues and policies, volume 6

AGRICULTURAL ISSUES AND POLICIES AGRICULTURAL ISSUES AND POLICIES VOLUME No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services AGRICULTURAL ISSUES AND POLICIES Additional books in this series can be found on Nova’s website under the Series tab Additional e-books in this series can be found on Nova’s website under the e-book tab AGRICULTURAL ISSUES AND POLICIES AGRICULTURAL ISSUES AND POLICIES VOLUME LINDSEY K WATSON EDITOR New York Copyright © 2016 by Nova Science Publishers, Inc All rights reserved No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication Simply navigate to this publication’s page on Nova’s website and locate the “Get Permission” button below the title description This button is linked directly to the title’s permission page on copyright.com Alternatively, you can visit copyright.com and search by title, ISBN, or ISSN For further questions about using the service on copyright.com, please contact: Copyright Clearance Center Phone: +1-(978) 750-8400 Fax: +1-(978) 750-4470 E-mail: info@copyright.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works Independent verification should be sought for any data, advice or recommendations contained in this book In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services If legal or any other expert assistance is required, the services of a competent person should be sought FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS Additional color graphics may be available in the e-book version of this book Library of Congress Cataloging-in-Publication Data ISBN:  (eBook) ISSN: 2165-2635 Published by Nova Science Publishers, Inc † New York CONTENTS Preface Chapter Chapter vii Farm Safety Net Programs: Background and Issues Dennis A Shields Specialty Crop Provisions in the 2014 Farm Bill (P.L 113-79) Renée Johnson Chapter Agricultural Research: Background and Issues Jim Monke Chapter Effects of Recent Energy Price Reductions on U.S Agriculture Kandice K Marshall, Stephanie M Riche, Ralph M Seeley and Paul C Westcott Chapter Index How Transportation Costs Affect Fresh Fruit and Vegetable Prices Richard Volpe, Edward Roeger and Ephraim Leibtag 29 77 95 119 159 PREFACE Agriculture is one of the defining elements of a nation This series consists of analyses on a variety of agricultural issues including (but not limited to) the developments, policies, programs, trade, trends and economics of agriculture Topics will be seen from a U.S perspective, but not to the exclusion of other countries particularly when the discussion has an international scope Topics discussed in this issue include farm safety net programs; specialty crop provision in the 2014 Farm Bill; background and issues in agricultural research; the effects of recent energy price reductions on U.S agriculture; and the effect of transportation costs on fresh fruit and vegetable prices In: Agricultural Issues and Policies Volume ISBN: 978-1-63484-103-0 Editor: Lindsey K Watson © 2016 Nova Science Publishers, Inc Chapter FARM SAFETY NET PROGRAMS: BACKGROUND AND ISSUES Dennis A Shields SUMMARY The U.S Department of Agriculture (USDA) operates several programs that supplement the income of farmers and ranchers in times of low farm prices and natural disasters Federal crop insurance, farm programs, and disaster assistance are collectively called the farm safety net Federal crop insurance is often referred to as the centerpiece of the farm safety net because of its cost and broad scope for addressing natural disasters The program is permanently authorized and makes available subsidized insurance for more than 130 commodities (ranging from apples to wheat) to help farmers manage risks associated with a loss in yield or revenue Program cost is projected by the Congressional Budget Office to total $8.8 billion per year over the next decade Producers pay a portion of the premium which increases as the level of coverage rises The federal government pays the rest of the premium—62%, on average, in 2014—and covers the cost of selling and servicing the policies Farm commodity programs historically represented the heart of U.S farm policy by virtue of their long history (dating back to the 1930s)  This is an edited, reformatted and augmented version of a Congressional Research Service publication R43758, prepared for Members and Committees of Congress, dated August 21, 2015 Table A2 Regression sample sizes and R2 Values for the multiple origin wholesale price analysis Sample sizes are listed first, followed by R2 values in parentheses “NA” signifies that this model was not used due to a small sample size 154 Richard Volpe, Edward Roeger and Ephraim Leibtag REFERENCES Andreyeva, T., M Long, and K Brownell 2010 “The Impact of Food Prices on Consumption: A Systematic Review of the Research on the Price Elasticity of the Demand for Food,” American Journal of Public Health 100(2):216-22 Ballenger, N., W Coyle, W Hall, B McGregor, and R Hawkins 1999 “Transportation Technology Eases the Journey for Perishables Going Abroad,” Agricultural Outlook U.S Department of Agriculture, Economic Research Service Calvin, L., and R Cook 2001 U.S Fresh Fruit and Vegetable Marketing: Emerging Trade Practices, Trends, and Issues Agricultural Economic Report 795, U.S Department of Agriculture, Economic Research Service Canning, P 2011 A Revised and Expanded Food Dollar Series: A Better Understanding of Our Food Costs Economic Research Report 114., U.S Department of Agriculture, Economic Research Service Cook, R 2002 “The Dynamic U.S Fresh Produce Industry: An Industry in Transition.” Chapter in A Kader (ed.), Postharvest Technology of Horticultural Crops, Third Edition University of California at Davis, Division of Agriculture and Natural Resources, Publication 3311 Denicoff, M., E Jessup, A Taylor, and D Nibarger 2010 “The Importance of Freight Transportation to Agriculture.” Chapter in Study of Rural Transportation Issues, Congressionally mandated study produced by the U.S Department of Agriculture and the U.S Department of Transportation Gilbert, C 2010 “How to Understand High Food Prices,” Journal of Agricultural Economics 61(2):398-425 Goodwin, H., S Fuller, O Capps, and O Asgill 1988 “Factors Affecting Fresh Potato Price in Selected Terminal Markets,” Western Journal of Agricultural Economics 13(2):233-43 Hendrickson, J 1994 Energy Use in the U.S Food System: a Summary of Existing Research and Analysis Center for Integrated Agricultural Systems, University of Wisconsin-Madison, Madison, WI Huang, S., and K Huang 2007 Increased U.S Imports of Fresh Fruit and Vegetables Outlook FTS 328-01, U.S Department of Agriculture, Economic Research Service Lucier, G., S Pollack, M Ali, and A Perez 2006 Fruit and Vegetable Backgrounder Outlook VGS 313-01, U.S Department of Agriculture, Economic Research Service How Transportation Costs Affect Fresh Fruit and Vegetable Prices 155 Martinez, S., M Hand, M Da Pra, S Pollock, K Ralston, T Smith, S Vogel, S Clark, L Lohr, S Low, and C Newman 2010 Local Food Systems: Concepts, Impacts, and Issues Economic Research Report 97 U.S Department of Agriculture, Economic Research Service Pirog, R., T Van Pelt, K Enshayan, and E Cook 2001 Food Fuel, and Freeways: An Iowa Perspective on How Far Food Travels, Fuel Usage, and Greenhouse Gas Emissions Leopold Center for Sustainable Agriculture, Ames, IA Pollack, S 2001 “Consumer Demand for Fruit and Vegetables: The U.S Example.” Chapter in A Regmi (ed.), Changing Structure of Global Food Consumption and Trade Agriculture and Trade Report WRS 01-1 U.S Department of Agriculture, Economic Research Service Raper, K., S Thornsbury, and C Aguilar 2009 “Regional Wholesale Price Relationships in the Presence of Counter-Seasonal Imports,” Journal of Agricultural and Applied Economics 41(1):271-90 Sands, R., P Westcott, M Price, J Beckman, E Leibtag, G Lucier, W McBride, D McGranahan, M Morehart, E Roeger, G Schaible, and T Wojan 2011 Impacts of Higher Energy Prices on Agriculture and Rural Economies Economic Research Report 123, U.S Department of Agriculture, Economic Research Service, Washington, DC Tegene, A 2009 “High and Volatile Commodity Prices: What Do They Mean for Food Prices and Consumers: Discussion,” American Journal of Agricultural Economics 91(5):1468-69 U.S Department of Agriculture, Agricultural Marketing Service 2009 Agricultural Refrigerated Truck Quarterly RTQ-4th Quarter U.S Department of Agriculture, Washington, DC Weber, C., and H Matthews 2008 “Food-Miles and the Relative Climate Impacts of Food Choices in the United States,” Environmental Science and Technology 42(10):3508-13 Wendt, M., and J Todd 2011 The Effect of Food and Beverage Prices on Children’s Weights Economic Research Report 118 U.S Department of Agriculture, Economic Research Service, Washington, DC Wohlgenant, M 2001 “Marketing Margins: Empirical Analysis.” Chapter in B Gardner and G Rausser (eds.), Handbook of Agricultural Economics, Elsevier Science 156 Richard Volpe, Edward Roeger and Ephraim Leibtag End Notes According to Consumer Price Index data from the Bureau of Labor Statistics, motor fuel prices changed (either up or down) by an average of 11.5 percent per year between 1990 and 2011, while overall food prices increased by 2.7 percent per year Numbers are based on the 2007-08 average of USDA Economic Research Service (ERS) Food Availability Data System (FADS) farm weights and consider cantaloupes in the fresh fruit total An exception to this is Peruvian asparagus, which transitioned from being mostly transported by boat (85.8 percent) in 1999 to being almost completely shipped by air (93.9 percent) in 2003 and then back to a mixture of ocean and air transport as fuel prices increased from 2004 through 2009 (air transport share was 86.3 percent in 2004 and 57.8 percent in 2008) “Ordinary” in this sense is defined by AMS as “having a heavy percentage of defects as compared with “good.” Organic produce is dropped because price observations of this type are generally not uniformly distributed across years or markets and not present in large numbers When analyzed, organic certification exhibits a very large positive and statistically significant effect on prices For asparagus and oranges, this trend is weaker in Chicago than in New York, likely due to the Chicago market having less California-grown produce Fruit and Vegetables Programs, Market News Branch Truck transport is commonly cited as the dominant mode of domestic transportation for fresh fruits and vegetables; 94 percent of such transport was by truck in 2002 (Denicoff et al., 2010) The authors not feel the assumption of a 38,000-pound load to be significantly strong because, if applied to each observation, the assumption would affect the magnitudes of the results but not the relationships within the results Dollars per pound is used here instead of other price measures (i.e., total route cost, dollars per mile) because it enables direct comparisons to produce prices, which are also expressed in dollars per pound 10 The seasonal dummy variables divide the calendar year into quarters They correspond to January through March, April through June, July through September, and October through December 11 These variables vary slightly by commodity Using conventions drawn from and assigned by AMS, they describe the quality, appearance, condition, and, when applicable, the size and color of the produce being shipped The full list of descriptors is available from the authors upon request 12 We tested each time series value for the presence of a unit root using the Augmented DickeyFuller test Unit roots present problems because they indicate that regression results may be spurious While the inclusion of a time trend typically attenuates concerns in this regard, the truck rate series for Dallas and Los Angeles strongly indicated the presence of unit roots As a result, we estimate the equations for these cities on first-differenced data 13 We also experimented with a yearly fixed-effects estimation This approach yielded a number of counterintuitive results, likely stemming from the fact that several of these commodities undergo growing seasons or peak distribution periods that overlap calendar years Given that it is not clear how to most accurately delineate years on a per commodity basis, any approach in this respect would necessarily be ad hoc How Transportation Costs Affect Fresh Fruit and Vegetable Prices 157 14 Due to the large number of routes in this study and the primary focus on fuel price effects, only coefficients for the fuel price variables will be listed in the results tables Results for the other variables are available upon request 15 The reason for the nonlinear relationships likely has to with different demands for the different routes and differing possibilities for back-hauling from the destination cities 16 Using the reported standard errors, it is possible to construct 95-percent confidence intervals for the diesel price coefficients The estimated Boston, New York, Philadelphia, Miami, Atlanta, and Chicago coefficients are not statistically different from one another The Dallas coefficient is statistically smaller than those for Boston, New York, Philadelphia, and Miami 17 Although this effect would not be expected, in practice, given that the coefficient on diesel prices is not significant 18 For grapes, prices are not matched on size due to the large number of size and variety combinations possible in the markets and the consequent lower number of price matches when both dimensions are used 19 This monthly aggregate supply variable estimates a basic accounting of the total U.S supply for a given fresh produce item by combining monthly import and export data from the USDA Foreign Agricultural Service (FAS) with annual U.S production data from the USDA-ERS’s Food Availability Data System, which has been has been scaled to monthly numbers using domestic movement data from AMS 20 Many empirical approaches to studying marketing margins have employed structural models, examining supply and demand simultaneously in order to identify key economic relationships (Wohlgenant, 2001) Our markets of interest benefit from highly inelastic supply in the short term, but nevertheless, in this reduced-form setting the shifters for seasonality must be thought of as proxies for changes in demand In practice, a wide number of factors other than price can influence demand 21 The markets are generally categorized by region in a geographic manner, with assignments as follows: California—Los Angeles and San Francisco; Midwest—Detroit, Chicago, and St Louis; Southeast—Atlanta, Columbia, and Baltimore; Northeast—Boston, New York, Philadelphia, and Pittsburgh Geographically, Baltimore may fit more closely in the Northeast, but the results for this market are much closer to the Atlanta market in both produce price analyses 22 The CV is the sample standard deviation of a variable divided by the sample mean It is a unitless measure of dispersion that can be interpreted as a percentage and is ideal for comparing dispersion across samples with different units or means 23 The routes are organized by origin in this analysis because of both the clear need to account for origin-specific fixed effects and fuel price coefficients and for the greater flexibility of allowing the season and supply variables to vary significantly based on the origin 24 This more general fuel price is used here rather than a diesel price (even though diesel is a common fuel in truck and ocean transport) because of the greater variety of origins (and thus different fuel types) represented in this analysis 25 Oranges also fit closely with this last statement, since imports generally make up a small share of offerings in all markets and domestic supply is primarily from Florida or California, with offerings from Florida tending to show up more in East Coast markets 26 There are also a few cases where some dispersion exists with a region and origin group; possible causes for some of these instances will be discussed later 27 Central America in this study refers to the countries of Costa Rica, Guatemala, and Honduras 158 28 Richard Volpe, Edward Roeger and Ephraim Leibtag Summary of AMS Movement Reports, 2000 through 2009 Available online at http://www.marketnews.usda.gov/ portal/fv 29 This is assuming no factor substitution in production 30 See, for example, Goodwin et al (1988) Additionally, Wohlgenant (2001) provides a review of the literature up to that date on the economic analysis of marketing margins INDEX A access, 50 accounting, 92, 99, 142, 157 acquisitions, 47 adaptation, 86, 91 adjustment, 107 advancements, 14 adverse weather, 4, agencies, 30, 43, 62, 67, 77, 78, 79, 80, 81, 85, 87, 88, 127, 128 aggregate supply, 157 aggregation, 138 agricultural economics, 79 agricultural exports, 29, 31 agricultural market, 11, 79, 98, 108 agricultural producers, 30, 37, 51, 70 Agricultural Research Service (ARS), 43, 77, 78, 80, 81 agricultural sector, 11, 86, 88, 90, 91, 95, 97, 101, 104, 105, 106, 107 agriculture, vii, 2, 3, 11, 16, 27, 29, 30, 31, 34, 35, 36, 38, 43, 52, 56, 77, 78, 79, 80, 85, 90, 91, 92, 93, 96, 100, 101, 104, 113 alfalfa, 15 almonds, 15 alternative energy, 105, 107 alters, ammonia, 100 apples, 1, 15, 57 appropriations, 10, 36, 37, 49, 50, 51, 52, 53, 54, 56, 57, 58, 60, 62, 64, 70, 76, 79, 80, 81, 82, 83, 85 aquaculture, 10 arbitrage, 142 ARC, 2, 3, 7, 8, 12, 13, 17, 18, 20, 21, 22, 23, 24, 26, 27, 44, 72 ARS, 78, 81, 86, 88 asparagus, 119, 121, 124, 125, 126, 129, 130, 131, 132, 133, 138, 139, 140, 146, 149, 151, 156 assessment, 60 assets, atmosphere, 129 authority(s), 10, 38, 48, 50, 51, 52, 53, 56, 58, 60, 65, 68, 73, 78, 79, 84 authorized programs, 11 automobiles, 109 B bankruptcy, 16 barriers, 46 base, 7, 12, 17, 18, 19, 21, 22, 26, 27, 44, 76 basic research, 80, 83, 85 beef, 98 benefits, 6, 8, 16, 20, 22, 34, 35, 44, 50, 91 biodiesel, 96 bioenergy, 116 biofuel, 95, 96, 97, 108, 109, 112, 113 160 Index biomass, 117 blend wall, 112 blends, 109, 114 boat, 156 Brazil, 7, 27 budget deficit, 2, 17 budget surplus, 86 Bureau of Labor Statistics, 156 business strategy, 34 businesses, 11 C caliber, 85 candidates, 85 cane sugar, capacity building, 82 cash, 4, cash flow, 4, catfish, 16 cattle, 102 cellulosic biofuel, 117 Census, 31, 32, 33, 76, 78, 82, 141 certification, 34, 38, 46, 57, 61, 156 challenges, 85, 88, 90, 91, 117 chemical(s), 96, 98, 99, 107 Chicago, 110, 127, 130, 133, 134, 135, 136, 137, 140, 144, 145, 152, 156, 157 child labor, 69 children, 49 Chile, 125, 126, 129, 144, 145, 146, 148 cities, 134, 135, 139, 156, 157 citizens, 64 classes, 14 classification, 20 climate, 19, 86, 88, 90, 91 climate change, 88, 90 cocoa, 68 coefficient of variation, 139, 140, 141 coffee, 68 collaboration, 63 collateral, 8, 13 colleges, 80, 83 color, 156 commerce, 57, 69 commercial, 32, 125 commodity markets, 102, 103, 139 commodity producers, 34 community(s), 49, 50, 56, 79, 85 compensation, competitiveness, 53, 69, 90 complexity, 15, 20 compliance, 20, 39, 112 conference, 76 Congress, 1, 2, 3, 6, 10, 11, 12, 16, 17, 29, 37, 59, 61, 63, 66, 67, 68, 69, 70, 71, 76, 77, 78, 80, 82, 87, 91 Congressional Budget Office, 1, 5, 38, 114 conservation, 8, 34, 37, 38, 46, 61, 78, 86, 90 conservation programs, 37 consumer expenditure, 149 Consumer Price Index, 156 consumers, 64, 122, 124, 143, 149 consumption, 29, 31, 109, 110, 114, 120, 123, 124, 126, 149, 151 consumption patterns, 124 containers, 129 contaminated food, 65 contamination, 64, 70 coordination, 53, 62, 80 correlation, 135, 146, 151 cost, 1, 3, 4, 5, 8, 9, 17, 38, 45, 48, 50, 54, 61, 76, 96, 97, 98, 100, 103, 106, 108, 109, 114, 122, 135, 156 Costa Rica, 126, 157 costs of production, 95, 98, 103, 107, 113 cotton, 4, 5, 6, 7, 8, 10, 14, 15, 18, 21, 26, 27, 76, 91, 98, 99, 102, 104, 106 covering, 53 crop insurance, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 20, 21, 23, 26, 30, 35, 36, 38, 42, 45, 61, 72, 74, 91 crop producers, 35, 38, 42, 72, 98 crop production, 18, 29, 31, 32, 34 CRP, 43 crude oil, 95, 96, 100, 103, 104, 108, 112, 116, 142 Customs and Border Protection, 59, 71 161 Index D data collection, 38 database, 60, 76 deaths, decoupling, 19 defects, 156 deficiency, deficit, 6, 92 demand curve, 110 Department of Agriculture, 155 Department of Commerce, 43 Department of Defense, 47 Department of Energy, 96, 123, 136 Department of Labor, 69 Department of Transportation, 154 dependent variable, 139 derivatives, 100 detection, 66 diesel fuel, 139 direct cost, 98 direct payment, 2, 10, 11, 20 disaster, 1, 2, 3, 4, 9, 10, 11, 12, 16, 17, 30, 35, 42, 45, 66, 74 disaster area, 10 disaster assistance, 1, 2, 11, 30, 35 disaster relief, 10 dispersion, 143, 157 disposition, 130 distortions, 18 distribution, 32, 65, 81, 85, 97, 101, 102, 112, 130, 156 diversification, 61 diversity, 52 drought, 4, 9, 15, 16 E E85, 109 economic growth, 88, 90 economic incentives, 104 economic problem, 11 economic relations, 157 Economic Research Service (ERS), 27, 39, 41, 77, 79, 80, 81,87, 88, 92, 95, 98, 99, 100, 102, 103, 108, 112, 114, 115, 116, 119, 123, 126, 127, 128, 129, 131, 132, 134, 154, 155, 156, 157 economics, vii, 52, 124 education, 53, 58, 61, 64, 70, 77, 80, 83, 86, 90, 91 educational programs, 79 egg, 80 electricity, 98, 99, 107 emergency, 3, 10, 49 Emergency Assistance, 9, 16, 27, 42, 45 employees, 78, 79, 81 energy, vii, 91, 95, 96, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 113, 114, 117, 121, 122, 124, 143, 147, 149 energy efficiency, 124 Energy Independence and Security Act, 117 energy input, 96, 98, 99 energy prices, 95, 96, 98, 100, 102, 103, 104, 106, 107, 108, 109, 113, 114, 117, 122, 124 enforcement, 60, 69 environment, 79, 147 Environmental Protection Agency (EPA), 43, 67, 68, 112, 117 environmental sustainability, 91 equilibrium, 98 equipment, 13, 54 ethanol, 8, 95, 96, 108, 109, 110, 111, 112, 113, 114, 117 exclusion, vii, 80 expenditures, 66, 86, 149, 151 export market, 46 exporters, 125 exports, 46 exposure, 68 extreme weather events, 90 F Fair Labor Standards Act, 69 families, 48 family members, 14 162 Index Farm Bill, v, vii, 7, 26, 27, 29, 34, 35, 36, 37, 41, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 75, 76, 91 farm size, 11 farmers, 1, 3, 4, 6, 11, 12, 13, 14, 15, 18, 19, 22, 29, 38, 56, 66, 74, 91, 98, 101, 113 farmland, 11, 34 farms, 9, 10, 11, 12, 13, 21, 31, 32, 33, 44, 65, 125 FAS, 43, 157 federal agency, federal assistance, federal crop insurance program, 5, 11, 19, 72 federal funds, 78, 86 federal government, 1, 5, 11, 91 federal law, 10 Federal Register, 27, 65 feedstock(s), 8, 95, 97, 113, 108, 109 fertilizers, 98 fiber, 11, 86 field crops, 5, 98, 102, 104 financial, 4, 13, 17, 46, 61 fiscal year, 46, 49, 51, 54, 61, 66 fish, Fish and Wildlife Service, 67 flexibility, 3, 8, 44, 47, 157 floods, 90 FLSA, 69 fluctuations, 123, 130, 132, 142, 147 food, 11, 30, 35, 37, 38, 43, 48, 49, 50, 54, 56, 63, 64, 65, 66, 68, 70, 76, 78, 79, 86, 90, 91, 101, 105, 106, 120, 122, 123, 124, 147, 149, 151, 156 Food and Drug Administration (FDA), 39, 43, 64, 65, 66 food production, 63, 122 food products, 56, 63 food safety, 30, 38, 43, 54, 65, 70, 78, 79, 90 Food, Conservation, and Energy Act of 2008, 30, 34, 76 foodborne illness, 65 forage crops, force, 85 forecasting, 151 foreign companies, 65 formula, 18, 77, 78, 79, 82, 83, 85, 86 frost, 42, 74 fruits, 3, 10, 29, 31, 32, 33, 34, 35, 39, 42, 43, 44, 47, 49, 50, 68, 70, 76, 119, 120, 122, 123, 124, 127, 129, 130, 148, 149, 150, 156 fuel prices, 104, 119, 120, 121, 122, 123, 124, 126, 134, 135, 137, 138, 139, 140, 141, 142, 143, 146, 147, 148, 149, 150, 151, 156 funding, 10, 12, 30, 35, 36, 37, 38, 45, 46, 47, 48, 49, 50, 52, 53, 54, 56, 60, 61, 62, 66, 73, 76, 77, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92 funds, 2, 10, 46, 48, 49, 50, 51, 52, 53, 56, 61, 62, 66, 77, 78, 79, 81, 82, 83, 85 G genome, 78 geography, 143 Germany, 116 global trade, 43 globalization, 11 government expenditure, 12 governments, 58, 70, 87 grants, 49, 50, 51, 52, 53, 54, 56, 57, 58, 61, 70, 77, 78, 79, 81, 82, 83, 85, 86 grass, 15 grazing, greed, 20 greenhouse, 31, 33 growth, 89, 90, 126, 147 Guatemala, 126, 157 guidance, 17 guidelines, 151 H habitat, 67 health, 50, 79, 90, 91, 123 Health and Human Services (HHS), 50 163 Index height, 108 higher education, 78, 83 history, 1, 6, 7, 15, 44 Honduras, 126, 157 honey bees, horticultural crops, 29, 32 House, 14, 19, 38, 43, 44, 45, 46, 48, 50, 52, 53, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 80 House of Representatives, 76 hub, 97, 101, 102 human, 78, 79, 86, 90, 91 human health, 79, 90 hurricanes, 10 hydrogen, 100 I imports, 65, 119, 121, 122, 123, 125, 126, 128, 130, 137, 139, 143, 146, 147, 151, 157 improvements, 90 income, 1, 2, 3, 6, 8, 10, 11, 14, 19, 30, 32, 35, 36, 48, 51, 90, 91 income support, 2, 3, 6, 11, 30, 35, 36, 91 independent variable, 137, 141, 145 individuals, 14, 83 industry(s), 5, 30, 35, 42, 43, 52, 58, 64, 66, 70, 86, 87, 108, 130, 149 inflation, 49, 86, 90 infrastructure, 49, 54, 117 institutions, 79, 80, 81, 83, 85, 86, 92 insurance policy, 15, 21 investment, 2, 88 Iowa, 111, 114, 155 Iran, 116 irrigation, 90, 98 issues, vii, 15, 20, 54, 70, 77, 80, 83, 112, 117, 120 L labeling, 60, 67 laws, 6, 38 legislation, 30, 37, 61, 76 light, 139, 141, 142, 145, 149 livestock, 2, 3, 4, 5, 9, 35, 37, 42, 43, 78, 80, 98, 117 loans, 3, 4, 7, 8, 10, 13, 26 local government, 56 Louisiana, 97, 101, 102 lower prices, 18, 96, 109, 113 M magnitude, 101, 103, 120 majority, 32, 80, 81, 87, 139, 142 management, 11, 13, 14, 53, 54, 61, 66, 67, 90 marketing, 2, 4, 7, 8, 13, 20, 26, 30, 35, 38, 39, 54, 58, 62, 63, 65, 70, 90, 119, 122, 130, 133, 134, 137, 139, 143, 150, 157, 158 meat, 34, 80 media, 27, 87, 89, 93, 114, 115 median, 130, 135 medicine, 80, 81 methodology, 98 Mexico, 125, 126, 128, 129, 142, 144, 145, 146, 148 Miami, 130, 135, 136, 137, 143, 144, 145, 147, 157 minimum price, 2, 3, 4, 6, 8, 18, 20, 72 minimum wage, 69 mission, 77, 78, 80, 85 models, 102, 125, 150, 157 mortality, motivation, 149 multiple factors, 109 mung bean, 44 J joint ventures, 14 jurisdiction, 43 N NAFTA, 128 164 Index NAP, 5, 42, 45, 73, 74 National Academy of Sciences (NAS), 68, 83, 85, 92 National Institutes of Health, 85 National School Lunch Program, 47, 48 natural disaster(s), 1, 3, 9, 10, 45 natural gas, 95, 96, 97, 100, 101, 102, 103, 109, 112, 113, 114 natural resource management, 78, 79, 88, 90 natural resources, 78, 79 Natural Resources Conservation Service (NRCS), 43, 61 neutral, 48 New Zealand, 128 nitrogen, 98, 100, 101 Noninsured Crop Disaster Assistance Program, nonprofit organizations, 56 North America, 128 North American Free Trade Agreement, 128 nutrition, 10, 30, 35, 38, 42, 47, 78, 79, 86, 90 O Obama, President Barack, 27 octane, 109, 114 OFPA, 60 oil, 96, 97, 98, 102, 103, 104, 107, 108, 109, 113, 114, 116, 122, 123, 131, 144, 146, 147, 148, 149, 151 oil production, 96 operating costs, 5, 103, 113 operations, 14, 31, 32, 34, 35, 46, 56, 60, 69 opportunities, 38, 56, 80, 90 Organic Foods Production Act, 60 ornamental plants, 76 outreach, 61 overlap, 27, 156 oversight, 65, 80 overtime, 69 ownership, 112 P participants, 6, 12, 42, 50, 124 pasture, 3, 4, 5, pathogens, 64, 91 penalties, 60 permit, 48 personal communication, 88 Peru, 125, 126, 129, 144, 148 pesticide, 67, 68, 107 pests, 54, 68, 91 petroleum, 96, 111, 116 Philadelphia, 130, 135, 136, 137, 140, 144, 145, 146, 152, 157 plants, 76 policy, 1, 6, 7, 11, 13, 14, 15, 17, 18, 20, 21, 23, 26, 34, 38, 72, 79, 80, 86, 101, 105, 106, 123 policy makers, 11, 15, 17, 20 policymakers, 90, 123, 147 population, 11, 90, 139, 141, 147 population growth, 90 port of entry, 150 portfolio, 70, 91 poultry, 4, 10, 16, 31, 80, 98 President, 10, 27, 85, 92, 93 prevention, 66 price changes, 120, 147, 148, 151 price effect, 101, 114, 120, 121, 122, 137, 139, 142, 143, 151, 157 Private Funding, 86 private sector, 70, 78, 87, 91 procurement, 39, 42 producers, 2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 15, 16, 17, 19, 20, 21, 27, 30, 32, 34, 35, 38, 39, 42, 43, 44, 45, 46, 53, 58, 60, 61, 70, 72, 74, 76, 97, 98, 112, 117 production costs, 13, 90, 95, 97, 98, 100, 103, 110, 114, 117, 123 profit, 102 profitability, 90, 109 program features, 18 project, 39, 47, 49, 54 protection, 4, 7, 8, 17, 19, 20, 42, 53, 78 pruning, 45 165 Index public health, 68 public investment, 90 public-private partnerships, 87 publishing, 65 Puerto Rico, 78, 80 Q quality standards, 30, 38 quotas, 4, R rangeland, 5, rate of return, 89 reasoning, 147 recall, 65 recession, 96, 108 recommendations, 67, 85, 90 reform(s), 34, 51, 64, 73 regional problem, 78 regression, 121, 124, 135, 137, 138, 139, 141, 145, 156 regression analysis, 121, 124 regression model, 135, 137, 138, 139 regulations, 12, 14, 58, 65, 66 regulatory requirements, 38 reimburse, 11 relative prices, 112 reliability, 85 remote sensing, 52 renewable energy, 79 renewable fuel, 112, 117 Renewable Fuel Standard (RFS), 109, 112, 114, 117 repair, 10 requirement(s), 14, 15, 43, 46, 47, 52, 60, 66, 67, 68, 70, 79, 83, 92, 98, 117 research funding, 82, 87, 91 researchers, 123 reserves, 96 residues, 68 resource management, 86 resources, 83, 86, 91, 92 response, 7, 95, 103, 104, 114 responsiveness, 149 retail, 29, 31, 50, 122, 123, 125, 130, 148, 149, 151 revenue, 1, 3, 4, 5, 7, 13, 14, 18, 19, 20, 21, 24, 25, 72 rewards, 11 RFS, 109, 112, 117 risk(s), 1, 2, 3, 4, 11, 14, 15, 20, 48, 61, 65, 78, 86, 90 risk management, 11, 61, 90 root(s), 156 routes, 121, 134, 135, 141, 142, 147, 151, 157 rules, 8, 20 rural development, 38, 78, 79, 90 rural population, 79, 82 S safety, vii, 1, 2, 3, 4, 11, 12, 16, 17, 20, 65 sample mean, 157 sanctions, 116 savings, 12, 20 school, 10, 47, 48, 49, 80, 81 science, 68 scientific knowledge, 77, 78 SCO, 3, 14, 15, 20, 21, 23, 72 scope, vii, 1, 4, 35, 52, 56, 137, 149 seasonal component, 135 seasonality, 120, 121, 125, 138, 157 Secretary of Agriculture, 10 seed, 6, 7, 13, 15, 68 Senate, 14, 19, 27, 38, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 56, 57, 58, 60, 62, 63, 64, 66, 68, 70, 72, 73, 74, 80 sensitivity, 120, 121, 124, 126, 135, 139, 140, 143, 146, 147, 150, 151 service provider, 49 services, 27, 30, 35 showing, 106, 147 simulation, 105, 106 SNAP, 37, 42 social sciences, 78 society, 11, 12 166 Index soil erosion, 34 solution, 102 South Africa, 126, 128 soybeans, 2, 3, 5, 6, 10, 15, 19, 21, 24, 30, 34, 76, 91, 98, 99, 101, 106 specialty crop, vii, 5, 29, 30, 31, 32, 34, 35, 36, 37, 38, 41, 42, 43, 46, 52, 54, 57, 62, 66, 69, 70, 76, 80 species, 67 spending, 2, 17, 20, 30, 35, 36, 37, 38, 42, 51, 76, 86, 87, 88, 90, 91 stability, 11 stakeholder groups, 90 standard deviation, 141, 157 standard error, 137, 157 state(s), 32, 33, 44, 47, 49, 54, 58, 61, 70, 77, 78, 79, 80, 82, 84, 85, 86, 87, 91, 92 statistics, 78, 133, 134, 139, 142, 152 statutes, 80 statutory authority, statutory provisions, 24, 25, 26 stock, 8, 45, 66 storage, 125, 150 structure, 90 style, 20 subsidy(s), 6, 8, 11, 12, 17, 18, 19 substitution, 158 sugarcane, 117 Supplemental Nutrition Assistance Program (SNAP), 37, 42, 49 supplier(s), 47, 137, 138, 140, 143, 146 supply chain, 122, 124 supply curve, 109, 110 surplus, 10 surveillance, 54, 65, 66, 86 sustainability, 58, 70 technical change, 53 technical support, 61 techniques, 11 technological advances, 129 technology(s), 60, 79, 87, 90, 91, 129 technology transfer, 87 territory, 54, 91 terrorism, 87 threats, 80, 91 time periods, 143 time series, 156 Title I, 39, 41, 42, 43, 44, 45, 46, 47, 56, 59, 72 Title II, 39, 41, 42, 43, 45, 46, 59 Title IV, 39, 42, 47, 56 Title V, 39, 41, 42, 50, 51, 67, 76, 80 total energy, 98, 107 total product, 107 tourism, 56 trade, vii, 7, 18, 19, 20, 27, 30, 35, 37, 38, 46, 90, 119, 130, 149 trade costs, 149 trade policy, 19 trading partners, 128 transmission, 124, 148 transport, 120, 121, 122, 123, 124, 126, 129, 134, 135, 137, 140, 141, 142, 143, 147, 150, 151, 156, 157 transport costs, 137, 140, 150 transportation, vii, 2, 20, 95, 117, 119, 120, 121, 122, 124, 125, 126, 129, 134, 135, 137, 139, 143, 149, 150, 156 transportation infrastructure, 2, 20 Treasury, 50 tribal colleges, 80 triggers, 17 Trust Fund, 39, 45, 52 T U TAP, 9, 10, 42, 45 target, 64 Task Force, 54, 90, 92 tax credits, 50 taxpayers, 11 technical assistance, 46, 59, 66, 71 U.S Department of Agriculture (USDA), 1, 3, 5, 29, 31, 77, 78, 82, 92, 95, 114, 115, 119, 123, 126, 127, 128, 129, 131, 132, 134, 136, 154, 155 U.S policy, 20 167 Index uniform, 130 United Nations, 93, 116 United States, 20, 32, 34, 59, 65, 71, 72, 80, 89, 92, 108, 109, 112, 117, 120, 121, 122, 123, 124, 125, 126, 130, 143, 147, 155 universities, 12, 80, 83, 85, 87, 92 urban, 91 Uruguay, 20 Uruguay Round, 20 V variable costs, 110 variables, 102, 130, 135, 138, 150, 156, 157 variations, 20, 122, 142 varieties, 90, 138 vector, 135, 138 vegetables, 3, 4, 10, 29, 31, 32, 34, 35, 39, 42, 43, 44, 47, 49, 50, 70, 76, 119, 120, 122, 123, 124, 127, 129, 130, 148, 149, 150, 156 vein, 124 volatility, 11, 120, 124, 139, 141, 147, 149, 151 W Washington, 32, 79, 80, 92, 93, 155 waste, 11 water, 9, 86, 91, 124 water shortages, well-being, 79 wholesale, 110, 119, 120, 121, 122, 123, 124, 125, 127, 130, 132, 133, 134, 137, 138, 139, 140, 141, 142, 143, 146, 147, 148, 149, 150, 151, 152, 153 Wisconsin, 154 withdrawal, 68 wool, 4, World Trade Organization (WTO), 19, 20 World War I, 89 Y yield, 1, 3, 4, 5, 12, 14, 15, 19, 20, 21, 24, 25, 72, 74 ... Nova’s website under the e-book tab AGRICULTURAL ISSUES AND POLICIES AGRICULTURAL ISSUES AND POLICIES VOLUME LINDSEY K WATSON EDITOR New York Copyright © 20 16 by Nova Science Publishers, Inc All.. .AGRICULTURAL ISSUES AND POLICIES AGRICULTURAL ISSUES AND POLICIES VOLUME No part of this digital document may be reproduced, stored... In: Agricultural Issues and Policies Volume ISBN: 978-1 -63 484-103-0 Editor: Lindsey K Watson © 20 16 Nova Science Publishers, Inc Chapter FARM SAFETY NET PROGRAMS: BACKGROUND AND ISSUES Dennis