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2021 | OriginalPaper | Chapter

Nutrient Use and Precision Agriculture in Corn Production in the USA

Authors : Roberto Mosheim, David Schimmelpfennig

Published in: Advances in Efficiency and Productivity Analysis

Publisher: Springer International Publishing

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Abstract

This is a timely study of precision agriculture as both data management (mapping) and field production technologies for agricultural production are changing rapidly. We compare the performance of producers who adopt precision agriculture tools versus those that do not. We estimate both their own frontier performance and a metafrontier that enables the research to compare the efficiency of producers across technologies. To make these comparisons we pre-processed the data with a matching procedure in order to have a sample of producers of equal size for each category who faced similar conditions. In the metafrontier results we find that GPS yield maps, guidance auto-steering precision agriculture technologies, and managerial ability save input costs and increase farm production efficiency which has environmental benefits. Maps created from soils or aerial data and input applications using VRT did not produce useable results.

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Footnotes
1
This survey method means that each sample farm represents multiple farms from the same state and size class, and that the stratum weights have to be adjusted for nonresponse. Samples are expanded to population estimates with sample weights.
 
2
O’Donnell’s (2018, personal communication), suggestion.
 
Literature
go back to reference Amsler, C., Donnell, C. O.’., & Schmidt, P. (2017). Stochastic metafrontiers. Econometric Reviews, 36(6–9), 1007–1020.CrossRef Amsler, C., Donnell, C. O.’., & Schmidt, P. (2017). Stochastic metafrontiers. Econometric Reviews, 36(6–9), 1007–1020.CrossRef
go back to reference Battese, G. E., & Prasada Rao, D. S. (2002). Technology gap, efficiency, and a stochastic metafrontier function. International Journal of Business and Economics, 1(2), 87–93. Battese, G. E., & Prasada Rao, D. S. (2002). Technology gap, efficiency, and a stochastic metafrontier function. International Journal of Business and Economics, 1(2), 87–93.
go back to reference Battese, G. E., Rao, D. S., & Donnell, C. O’. (2003). Metafrontier functions for the study of inter-regional productivity differences (Working Paper Series No. 01/2003). Centre for Efficiency and Productivity Analysis. Battese, G. E., Rao, D. S., & Donnell, C. O’. (2003). Metafrontier functions for the study of inter-regional productivity differences (Working Paper Series No. 01/2003). Centre for Efficiency and Productivity Analysis.
go back to reference Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20, 325–332.CrossRef Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20, 325–332.CrossRef
go back to reference Bravo-Ureta, B. E., Greene, W., & Solis, D. (2012). Technical efficiency analysis correcting for biases from observed and unobserved variables: An application to a natural resource management project. Empirical Economics, 43(1), 55–72.CrossRef Bravo-Ureta, B. E., Greene, W., & Solis, D. (2012). Technical efficiency analysis correcting for biases from observed and unobserved variables: An application to a natural resource management project. Empirical Economics, 43(1), 55–72.CrossRef
go back to reference Coelli, T., Estache, A., & Trujillo, L. (2003). A primer on efficiency measurement for utilities and transport regulators. Washington, DC: World Bank Institute.CrossRef Coelli, T., Estache, A., & Trujillo, L. (2003). A primer on efficiency measurement for utilities and transport regulators. Washington, DC: World Bank Institute.CrossRef
go back to reference Coelli, T., Rao, D. S., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). New York, NY: Springer. Coelli, T., Rao, D. S., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). New York, NY: Springer.
go back to reference Condon, L. (2018, September 27). Crop Science Division, Bayer AG, 2018 Wall Street Journal, Global Food Forum, NY. Condon, L. (2018, September 27). Crop Science Division, Bayer AG, 2018 Wall Street Journal, Global Food Forum, NY.
go back to reference Erickson, B., & Lowenberg-DeBoer, J. (2017). 2017 Purdue Dealer Survey, CropLife. Erickson, B., & Lowenberg-DeBoer, J. (2017). 2017 Purdue Dealer Survey, CropLife.
go back to reference Greene, W. (2008). The econometric approach to efficiency analysis. In H. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth (pp. 92–250). New York, NY: Oxford University Press.CrossRef Greene, W. (2008). The econometric approach to efficiency analysis. In H. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth (pp. 92–250). New York, NY: Oxford University Press.CrossRef
go back to reference Griffin, T. W., Lowenberg-DeBoer, J., Lambert, D. M., Peone, J., Payne, T., & Daberkow, S. G. (2004). Adoption, profitability, and making better use of precision farming data (Staff Paper #04–06). Dept. of Agricultural Economics, Purdue University. Griffin, T. W., Lowenberg-DeBoer, J., Lambert, D. M., Peone, J., Payne, T., & Daberkow, S. G. (2004). Adoption, profitability, and making better use of precision farming data (Staff Paper #04–06). Dept. of Agricultural Economics, Purdue University.
go back to reference Griliches, Z. (1957). Hybrid corn: An exploration in the economics of technical change. Econometrica, 25(4), 501–522.CrossRef Griliches, Z. (1957). Hybrid corn: An exploration in the economics of technical change. Econometrica, 25(4), 501–522.CrossRef
go back to reference Hadri, K., Guermat, C., & Whittaker, J. (1999). Doubly heteroscedastic stochastic production frontiers with an English cereal farms (Discussion Paper 99–08). University of Exeter, School of Business and Economics. Hadri, K., Guermat, C., & Whittaker, J. (1999). Doubly heteroscedastic stochastic production frontiers with an English cereal farms (Discussion Paper 99–08). University of Exeter, School of Business and Economics.
go back to reference Hadri, K., Guermat, C., & Whittaker, J. (2003a). Estimation of technical inefficiency effects using panel data and doubly heteroscedastic stochastic production frontiers. Empirical Economics, 28(1), 203–222.CrossRef Hadri, K., Guermat, C., & Whittaker, J. (2003a). Estimation of technical inefficiency effects using panel data and doubly heteroscedastic stochastic production frontiers. Empirical Economics, 28(1), 203–222.CrossRef
go back to reference Hadri, K., Guermat, C., & Whittaker, J. (2003b). Estimating farm efficiency in the presence of double heteroscedasticity using panel data. Journal of Applied Economics, 6(2), 255–268.CrossRef Hadri, K., Guermat, C., & Whittaker, J. (2003b). Estimating farm efficiency in the presence of double heteroscedasticity using panel data. Journal of Applied Economics, 6(2), 255–268.CrossRef
go back to reference Hayami, Y. (1969). Sources of agricultural productivity gap among selected countries. American Journal of Agricultural Economics, 51(3), 564–575.CrossRef Hayami, Y. (1969). Sources of agricultural productivity gap among selected countries. American Journal of Agricultural Economics, 51(3), 564–575.CrossRef
go back to reference Hayami, Y., & Ruttan, V. W. (1970). Agricultural productivity differences among countries. American Economic Review., 40, 895–911. Hayami, Y., & Ruttan, V. W. (1970). Agricultural productivity differences among countries. American Economic Review., 40, 895–911.
go back to reference Henningsen, A. Mpeta, D., Daniel, F., Adem, A, Anwar, J. K., & Czekaj, T, et al. (2015). A meta-frontier approach for causal inference in productivity analysis: The effect of contract farming on sunflower productivity in Tanzania. 2015 AAEA & WAEA joint annual meeting, July 26-28, San Francisco, CA. Henningsen, A. Mpeta, D., Daniel, F., Adem, A, Anwar, J. K., & Czekaj, T, et al. (2015). A meta-frontier approach for causal inference in productivity analysis: The effect of contract farming on sunflower productivity in Tanzania. 2015 AAEA & WAEA joint annual meeting, July 26-28, San Francisco, CA.
go back to reference Huang, C., Huang, T. H., & Liu, N. (2014). A new approach to estimating the metafrontier production function based on a stochastic frontier framework. Journal of Productivity Analysis, 42(3), 241–254.CrossRef Huang, C., Huang, T. H., & Liu, N. (2014). A new approach to estimating the metafrontier production function based on a stochastic frontier framework. Journal of Productivity Analysis, 42(3), 241–254.CrossRef
go back to reference Schimmelpfennig, D. (2016). Farm profits and adoption of precision agriculture (Economic Research Report ERR-217). U.S. Department of Agriculture, p. 46. Schimmelpfennig, D. (2016). Farm profits and adoption of precision agriculture (Economic Research Report ERR-217). U.S. Department of Agriculture, p. 46.
go back to reference Schimmelpfennig, D. (2018). Crop production costs, profits, and ecosystem stewardship with precision agriculture. Journal of Agricultural and Applied Economics, 50(1), 81–103.CrossRef Schimmelpfennig, D. (2018). Crop production costs, profits, and ecosystem stewardship with precision agriculture. Journal of Agricultural and Applied Economics, 50(1), 81–103.CrossRef
go back to reference Survey, A. R. M. (2016). United States Department of Agricluture, Washington D.C. 20250, November. 2016. In ARMS 3 agricultural resource management Survey phase 3 Interviewer’s manual. Survey, A. R. M. (2016). United States Department of Agricluture, Washington D.C. 20250, November. 2016. In ARMS 3 agricultural resource management Survey phase 3 Interviewer’s manual.
go back to reference Swinton, S. M., & Lowenberg-DeBoer, J. (1998). Evaluating the profitability of site-specific farming. Journal of Production Agriculture, 11(4), 439–446.CrossRef Swinton, S. M., & Lowenberg-DeBoer, J. (1998). Evaluating the profitability of site-specific farming. Journal of Production Agriculture, 11(4), 439–446.CrossRef
Metadata
Title
Nutrient Use and Precision Agriculture in Corn Production in the USA
Authors
Roberto Mosheim
David Schimmelpfennig
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-47106-4_15