Productivity and Efficiency of Precision Farming: The Case of Czech Cereal Production

Lukáš ČechuraZdeňka Žáková KroupováVladimír KostlivýMichaela Lekešová

DOI 10.7160/aol.2021.130302
No 3/2021, September
pp. 15-24

Čechura, L., Žáková Kroupová, Z., Kostlivý, V. and Lekešová, M. (2021) “Productivity and Efficiency of Precision Farming: The Case of Czech Cereal Production", AGRIS on-line Papers in Economics and Informatics, Vol. 13, No. 3, pp. 15-24. ISSN 1804-1930. DOI 10.7160/aol.2021.130302.

Abstract

The paper deals with the sources of competitiveness of Czech cereal production by considering precision farming technology and employing micro-level data collected in the FADN database for the period 2005–2018. The analysis is based on the stochastic frontier modelling of an input distance function in the specification of the four-component model, which currently represents the most advanced approach to technical efficiency analysis. To provide a robust estimate of the model, the paper employs methods which control for the potential endogeneity of netputs in the four-step estimation procedure. Furthermore, the total factor productivity change is calculated using the Törnqvist-Theil index. The results reveal that Czech cereal producers took great advantage of their production possibilities and experienced technological progress, which contributed considerably to productivity dynamics and consequently to an increase in their competitiveness. Precision farming, which is associated with a large number of innovations reflected in technological change and optimal resource use, contributed to higher technical efficiency connected with cost savings in Czech cereal production.

Keywords

Total factor productivity, technical efficiency, precision farming, technology, cereal production.

References

  1. Addo, F. and Salhofer, K. (2019) "Determinants of Persistent and Transient Technical Efficiency of Austrian Crop Farms", Paper prepared for presentation at the 59th Annual Conference, Braunschweig, Germany, September 25-27, 2019 [Online]. Available: https://ageconsearch.umn.edu/record/292287/ [Accessed: 23 Jan. 2021]. DOI 10.22004/ag.econ.292287.
  2. Arellano, M. and Bover, O. (1995) "Another look at the instrumental variable estimation of error-components models", Journal of Econometrics, Vol. 68, pp. 29-51. ISSN 0304-4076. DOI 10.1016/0304-4076(94)01642-D.
  3. Balafoutis, A., Beck, B., Fountas, S., Vangeyte, J., van der Wal, T., Soto, I., Gómez-Barbero, M., Barnes, A. and Eory, V. (2017) "Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics", Sustainability, Vol. 9. ISSN 2071-1050. DOI 10.3390/su9081339.
  4. Bezlepkina, I. V., Oude Lansink, A. G. J. M. and Oskam, A. J. (2005) "Effects of subsidies in Russian dairy farming", Agricultural Economics, Vol. 33, No. 3, pp. 277-288. ISSN 0021-857X. DOI 10.1111/j.1574-0864.2005.00067.x.
  5. Blundell, R. and Bond, S. (1998) "Initial conditions and moment restrictions in dynamic panel data models", Journal of Econometrics, Vol. 87, pp. 115-143. ISSN 0304-4076. DOI 10.1016/S0304-4076(98)00009-8.
  6. Bokusheva, B. and Čechura, L. (2017) "Evaluating dynamics, sources and drivers of productivity growth at the farm level", OECD Food, Agriculture and Fisheries Papers, No. 106. Paris: OECD Publishing. E-ISSN 18156797. DOI 10.1787/5f2d0601-en.
  7. Caves, D. W., Christensen, L. R. and Diewert, W. E. (1982) "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity", Econometrica, Vol. 50, No. 6, pp. 1393-1414. ISSN 1468-0262. DOI 10.2307/1913388.
  8. Čechura, L., Grau, A., Hockmann, H., Levkovych, I. and Kroupová, Z. (2017) "Catching up or falling behind in Eastern European agriculture – the case of milk production", Journal of Agricultural Economics, Vol. 68, No. 1, pp. 206-227. ISSN 0021-857X. DOI 10.1111/1477-9552.12193.
  9. Čechura L., Kroupová Z. and Rudinskaya T. (2015) "Factors determining TFP changes in Czech agriculture", Agriculture Economics – Czech, Vol. 61, pp. 543-551. ISSN 1805-9295. DOI 10.17221/14/2015-AGRICECON.
  10. Chambers, R. G. (1988) "Applied Production Analysis", Cambridge: Cambridge University Press. 331 p. ISBN 0-521-31427-5.
  11. Cisternas, I., Velásquez, I., Caro, A. and Rodríguez, A. (2020) "Systematic literature review of implementations of precision agriculture", Computers and Electronics in Agriculture, Vol. 176, No. Sept, 105626. ISSN 0168-1699. DOI 10.1016/j.compag.2020.105626.
  12. Coelli, T. J., Rao, D. S. P., O’Donnell, Ch. J. and Battese, G. E. (2005) "An Introduction to Efficiency and Productivity Analysis", New York: Springer. 341 p. ISBN 987-0387-24265-1. DOI 10.1007/b136381.
  13. Colombi, R., Kumbhakar, S. C., Martini, G. and Vittadini, G. (2014) "Closed-skew normality in stochastic frontiers with individual effects and long/short-run efficiency", Journal of Productivity Analysis, Vol. 42, No. 2, pp. 123-136. ISSN 1573-0441. DOI 10.1007/s11123-014-0386-y.
  14. Diewert, W. (1976) "Exact and Superlative Index Numbers", Journal of Econometrics, Vol. 4, No. 2, pp. 115-145. ISSN 0304-4076. DOI 10.1016/0304-4076(76)90009-9.
  15. Filippini, M. and Greene, W. H. (2016) "Persistent and Transient Productive Inefficiency: A Maximum Simulated Likelihood Approach", Journal of Productivity Analysis, Vol. 45, pp.187-196. ISSN 1573-0441. DOI 10.1007/s11123-015-0446-y.
  16. Finger, R., Swinton, S. M., El Benni, N. and Walter, A. (2019) "Precision farming at the nexus of agricultural production and the environment", Annual Review of Resource Economics, Vol. 11, pp. 1-23. ISSN 1941-1340. DOI 10.1146/annurev-resource-100518-093929.
  17. N. D., O’Connell, Ch., Ray, D. K., West, P. C., Balzer, Ch., Bennett, E. M., Carpenter, S. R., Hill, J., Monfreda, Ch., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., Tilman, D. and Zaks, D. P. M. (2011) "Solutions for a cultivated planet", Nature, Vol. 478, No. 7369, pp. 337-342. ISSN 1476-4687. DOI 10.1038/nature10452.
  18. Gallardo, R.K., Sauer, J. (2018) "Adoption of Labor-Saving Technologies in Agriculture", Annual Review of Resource Economics, Vol. 10, pp. 185-206. ISSN 1941-1340. DOI 10.1146/annurev-resource-100517-023018.
  19. Gebbers, R. and Adamchuk, V. I. (2010) "Precision Agriculture and Food Security", Science, Vol. 327, No. 5967, pp. 828-831. ISSN 1095-9203. DOI 10.1126/science.1183899.
  20. Irmen, A. (2013) "Capital- and Labor-Saving Technical Change in an Aging Economy", International Economic Review, Vol. 58, No. 1., pp. 261-285. E-ISSN 1468-2354 DOI 10.1111/iere.12216.
  21. Jata, R. D., Jata, H. S., Nanwalb, R. K., Yadavc, A. K., Banad, A., Choudharye, K. M., Kakraliyab, S. K., Sutaliyaa, J. M., Sapkotaa, T. B. and Jat, M. L. (2018) "Conservation agriculture and precision nutrient management practices in maize-wheat system: Effects on crop and water productivity and economic profitability", Field Crops Research, Vol. 222, pp. 111-120. ISSN 0378-4290. DOI 10.1016/j.fcr.2018.03.025.
  22. Khanal, S., Fulton, J. and Shearer, S. (2017) "An overview of current and potential applications of thermal remote sensing in precision agriculture", Computers and Electronics in Agriculture, Vol. 139, pp. 22-2. ISSN 0168-1699. DOI 10.1016/j.compag.2017.05.001.
  23. Kornai, J. (1986) "The Soft Budget Constraint", Kyklos: International Review for Social Sciences, Vol. 39, No. 1, pp. 3-30. ISSN 1467-6435. DOI 10.1111/j.1467-6435.1986.tb01252.x.
  24. Kostlivý V., Fuksová Z. and Rudinskaya T. (2020) "Drivers of farm performance in Czech crop farms", Agriculture Economics – Czech, Vol. 66, pp. 297-306. ISSN 1805-9295. DOI 10.17221/231/2019-AGRICECON.
  25. Kumbhakar, S. C., Lien, G. and Hardaker, J. B. (2014) "Technical efficiency in competing panel data models: a study of Norwegian grain farming", Journal of Productivity Analysis, Vol. 41, No. 2, pp. 321-337. ISSN 1573-0441. DOI 10.1007/s11123-012-0303-1.
  26. Lien, G., Kumbhakar, S. C. and Alem, H. (2018) "Endogeneity, heterogeneity, and determinants of inefficiency in Norwegian crop-producing farms", International Journal of Production Economics, Vol. 201, pp. 53-61. ISSN 0925-5273. DOI 10.1016/j.ijpe.2018.04.023.
  27. Lovarelli, D., Bacenetti, J. and Guarino, M. (2020) "A review on dairy cattle farming: Is precision livestock farming the compromise for an environmental, economic and social sustainable production?", Journal of Cleaner Production, Vol. 262, 121409. ISSN 0959-6526. DOI 10.1016/j.jclepro.2020.121409.
  28. Loures, L., Chamizo, A., Ferreira, P., Loures, A., Castanho, R. and Panagopoulos, T. (2020) "Assessing the Effectiveness of Precision Agriculture Management Systems in Mediterranean Small Farms", Sustainability, Vol. 12, 3765. ISSN 2071-1050. DOI 10.3390/su12093765.
  29. Madau, F. A. (2007) "Technical Efficiency in Organic and Conventional Farming: Evidence form Italian Cereal Farms", Agricultural Economics Review, Vol. 8, No. 1, pp. 5-21. ISSN 1109-2580. DOI 10.22004/ag.econ.42141.
  30. Mankiw, N. G. (2009) "The principles of microeconomics", Mason: South-Western Cengage Learning, 545 p. ISBN 978-0-324-58998-6.
  31. Mintert, J., Widmar, D., Langemeier, M., Boehlje, M. and Erickson, B. (2016) "The challenges of precision agriculture: is big data the answer", Paper prepared for presentation at the Southern Agricultural Economics Association (SAEA) Annual Meeting, San Antonio, Texas, February 6-9, 2015. [Online]. Available: https://ageconsearch.umn.edu/record/230057/files/T%20...%20script_SAEA_2016.pdf [Accessed: 22 Jan. 2021]. DOI 10.22004/ag.econ.230057.
  32. National Research Council (1997) "Precision Agriculture in the 21st Century: Geospatial and Information Technologies in Crop Management", Washington, D.C., USA: National Academy Press. ISBN 978-0309058933. DOI 10.17226/5491.
  33. Njuki, E., and Bravo-Ureta, B. E. (2015) "The Economic Costs of Environmental Regulation in U.S. Dairy Farming: A Directional Distance Function Approach", American Journal of Agricultural Economics, Vol. 97, No. 4, pp. 1087-1106. ISSN 1467-8276. DOI 10.1093/ajae/aav007.
  34. Pisulewski, A. and Marzec, J. (2019) "Heterogeneity, transient and persistent technical efficiency of Polish crop farms", Spanish Journal of Agricultural Research, Vol. 17, No. 1. ISSN 2171-9292. DOI 10.5424/sjar/2019171-13926.
  35. Rudinskaya T., Hlavsa T. and Hruska M. (2019) "Estimation of technical efficiency of Czech farms operating in less favoured areas", Agriculture Economics – Czech, Vol. 65, pp. 445-453. ISSN 1805-9295. DOI 10.17221/52/2019-AGRICECON.
  36. Schrijver, R., Poppe, K. and Daheim, C. (2016) "Precision agriculture and the future of farming in Europe", Scientific Foresight Study IP/G/STOA/FWC/2013-1/Lot 7/SC5, Brussels: STOA, 38 p. ISBN 978-92-846-0475-3. DOI 10.2861/763030.
  37. Soto, I., Barnes, A., Balafoutis, A., Beck, B., Sánchez, B., Vangeyte, J., Fountas, S., Van der Wal, T., Eory, V. and Gómez-Barbero, M. (2019) "The contribution of Precision Agriculture Technologies to farm productivity and the mitigation of greenhouse gas emissions in the EU", JRC Technical Report, Luxembourg: Office of the European Union, 447 p. ISBN 978-92-79-92834-5. DOI 10.2760/016263.
  38. Torkya, M. and Hassaneinb, A. E. (2020) "Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges", Computers and Electronics in Agriculture, Vol. 178, 105476. ISSN 0168-1699. DOI 10.1016/j.compag.2020.105476.
  39. Tsionas, E. G. and Kumbhakar, S. C. (2014) "Firm heterogeneity, persistent and transient technical inefficiency: A generalized true random-effects model", Journal of Applied Econometrics, Vol. 29, No. 1, pp. 110-132. ISSN 1099-1255. DOI 10.1002/jae.2300.
  40. Zarco-Tejada, P. J., Hubbard, N. and Loudjani, P. (2014) "Precision Agriculture: an Opportunity for EU Farmers – Potential Support With the CAP 2014-2020", Study IP/B/AGRI/IC/2013_153, Brussels: European Parliament, 56 p. ISBN 978-92-823-5575-6.
  41. Zhang, N., Wang, M. and Wang, N. (2002) "Precision agriculture – a worldwide overview", Computers and Electronics in Agriculture, Vol. 36, No. 2-3, pp. 113-132. ISSN 0168-1699. DOI 10.1016/S0168-1699(02)00096-0.

Full paper

  Full paper (.pdf, 654.12 KB).