Published

2020-01-01

Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis

Cierre de brechas de rendimiento en los sistemas colombianos de siembra directa de arroz: un análisis de frontera estocástica

DOI:

https://doi.org/10.15446/agron.colomb.v38n1.79470

Keywords:

empirical models, food security, increasing rice demand, production function, technical efficiency (en)
modelos empíricos, seguridad alimentaria, aumento de la demanda de arroz, función de producción, eficiencia técnica (es)

Downloads

Authors

  • David Arango-Londoño Pontificia Universidad Javeriana Cali
  • Julián Ramírez-Villegas Consultative Group for International Agricultural Research (CGIAR); International Center for Tropical Agriculture (CIAT); University of Leeds
  • Camilo Barrios-Pérez Consultative Group for International Agricultural Research (CGIAR); International Center for Tropical Agriculture (CIAT); University of Tokyo
  • Osana Bonilla-Findji Consultative Group for International Agricultural Research (CGIAR); International Center for Tropical Agriculture (CIAT)
  • Andy Jarvis Consultative Group for International Agricultural Research (CGIAR); International Center for Tropical Agriculture (CIAT)
  • Jorge Mario Uribe Universidad del Valle - Cali

Rice is one of the most important crops in terms of harvested area and food security both globally and for Colombia. Improvement of technical efficiency levels in rice production in order to close yield gaps in a context in which rice demand increases, natural resources are depleted, and where there are growing expectations about both climate changes and trade agreements is likely the most important challenge that farmers confront. This research assessed the main management factors that limit both rice crop productivity and the likely drivers of non-optimal technical efficiency levels (a proxy for yield gaps). This study focused on both upland and irrigated direct seeding systems across a variety of environments in Colombia. Stochastic frontier models were used to integrate microeconomic theory and empirical regression analysis in conjunction with a large commercial rice production database developed by the Colombian rice growers’ federation (Fedearroz). A large variation was found in technical efficiency (from 40 to 95%) levels for both upland and irrigated systems, and major differences were obtained in the limiting factors of the two systems (e.g. seed availability, variety type, market accessibility, fertilizer type, and use rate). This suggests both substantial and varied opportunities for improvements in current technical efficiency levels. Across systems, the correct choice of variety was identified as a common key factor
for maximizing yield for a particular environment. For upland systems, optimal choices were F174 and F2000, whereas for irrigated rice F473 was found to produce the highest yield. Additionally, numerical analysis suggests a yield impact of ca. 0.18% for each 1% increase in the nitrogen application rate for upland systems. For irrigated rice, phosphorous rather than nitrogen application rates were found to be more important. Since our analysis is based on farm-scale commercial production data, we argue that once our results are brought to consensus with local extension agents, technicians and agronomists, then management recommendations for closing yield gaps can be used to improve rice productivity.

El cultivo de arroz es uno de los más importantes en términos de área cosechada y seguridad alimentaria tanto a nivel global como en Colombia. Mejorar los niveles de eficiencia técnica en la producción de arroz para cerrar las brechas de rendimiento en un contexto en el que la demanda de alimento aumenta, los recursos naturales escasean y las expectativas sobre los impactos potenciales del cambio climático y los tratados de libre comercio crecen es probablemente el desafío más importante que enfrentan los agricultores. Esta investigación evaluó los principales factores de gestión que limitan la productividad del cultivo de arroz, y los posibles impulsores de niveles de eficiencia técnica no óptimos (un proxy de las brechas de rendimiento). El estudio se enfocó en sistemas de siembra directa de tierras altas y de riego en diferentes ambientes en Colombia. Utilizamos modelos de frontera estocástica para integrar la teoría microeconómica y el análisis de regresión empírica junto con una gran base de datos de producción comercial de arroz, desarrollada por la Federación colombiana de productores de arroz (Fedearroz). Se encontró una gran variación en los niveles de eficiencia técnica (del 40 al 95%) para los sistemas de tierras altas y de riego, y se obtuvieron diferencias importantes en los factores limitantes entre los dos sistemas (por ejemplo, disponibilidad de semillas, tipo de variedad, accesibilidad al mercado, tipo de fertilizante, y tasa de uso). Esto sugiere oportunidades sustanciales y
variadas para mejorar los niveles actuales de eficiencia técnica. En todos
los sistemas, la elección correcta de la variedad se identificó como un factor clave común para maximizar el rendimiento por ambiente. Para los sistemas de tierras altas, las opciones óptimas fueron F174 y F2000, mientras que para el arroz de riego se encontró que F473 era el de mayor rendimiento. Además, el análisis numérico sugiere un impacto en el rendimiento de ca. 0.18% por cada 1% de aumento en la tasa de aplicación de nitrógeno para sistemas de tierras altas. Para el arroz de riego, se encontró que las tasas de aplicación de fósforo en lugar de nitrógeno eran más importantes. Como nuestro análisis se basa en datos de producción comercial a escala de finca, se argumentó que una vez que nuestros resultados llegan a un consenso con los agentes de extensión, técnicos y agrónomos locales, las recomendaciones de gestión para cerrar las brechas de rendimiento se pueden utilizar para mejorar la productividad del arroz.

References

Aigner, D., C.A.K. Lovell, and P. Schmidt. 1977. Formulation and estimation of stochastic frontier production function models. J. Econometrics 6, 21-37. Doi: 10.1016/0304-4076(77)90052-5

Bhatia, V.S., P. Singh, S.P. Wani, G.S. Chauhan, A.V.R.K. Rao, A.K. Mishra, and K. Srinivas. 2008. Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGROSoybean model. Agric. For. Meteorol. 148, 1252-1265. Doi: 10.1016/j.agrformet.2008.03.004

Chang, H.H. and F.I. Wen. 2011. Off-farm work, technical efficiency, and rice production risk in Taiwan. Agric. Econ. 42, 269-278. Doi: 10.1111/j.1574-0862.2010.00513.x

Coelli, T. and A. Henningsen. 2013. Frontier: stochastic frontier analysis. R package version 1.1-0. URL: https://CRAN.RProject.org/package=frontier (accessed July 2018).

DANE. 2004. Encuesta Nacional Agropecuaria. URL: https://www.dane.gov.co/files/investigaciones/boletines/ena/Bolet_Arroz_2004.pdf (accessed July 2018).

DANE. 2016. Statistical database. URL: https://www.dane.gov.co/files/investigaciones/agropecuario/censo-nacional-arrocero/boletin-tecnico-4to-censo-nacional-arrocero-2016.pdf (accessed July 2018).

Delerce, S., H. Dorado, A. Grillon, M.C. Rebolledo, S.D. Prager, V.H. Patiño, G. Garcés-Varón, and D. Jiménez. 2016. Assessing weather-yield relationships in rice at local scale using data mining approaches. PLOS One 11(89), e0161620. Doi: 10.1371/journal.pone.0161620

Esquivel, A., L. Llanos-Herrera, D. Agudelo, S.D. Prager, K. Fernandes, A. Rojas, J.J. Valencia, and J. Ramirez-Villegas. 2018. Predictability of seasonal precipitation across major crop growing areas in Colombia. Clim. Serv. 12, 36-47. Doi: 10.1016/j.cliser.2018.09.001

FAO. 2004. Rice and yield gap reduction, International rice year. Rome. URL: http://www.fao.org/3/Y5167E/y5167e02.htm (accessed July 2018).

FAO. 2010. The state of food insecurity in the world. Rome. URL: http://www.fao.org/publication /sofi/2010/en/ (accessed July 2018).

Foley, J.A., N. Ramankutty, K.A. Brauman, E.S. Cassidy, J.S. Gerber, M. Johnston, N.D. Mueller, C. O’Connell, D.K. Ray, P.C. West, C. Balzer, E.M. Bennett, S.R. Carpenter, J. Hill, C. Monfreda, S. Polasky, J. Rockstrom, J. Sheehan, S. Siebert, D. Tilman, and D.P.M. Zaks. 2011. Solutions for a cultivated planet. Nature 478, 337-342. Doi: 10.1038/nature10452

Fujisaka, S., L. Harrington, and P. Hobbs. 1994. Rice-Wheat in South Asia: systems and long-term priorities established through diagnostic research. Agric. Syst. 46, 169-187. Doi: 10.1016/0308-521X(94)90096-X

Heinemann, A.B., C. Barrios-Pérez, J. Ramírez-Villegas, D. Arango-Londoño, O. Bonilla-Findji, J.C. Medeiros, and A. Jarvis. 2015. Variation and impact of drought-stress patterns across upland rice target population of environments in Brazil. J. Exp. Bot. Doi: 10.1093/jxb/erv126

ISRIC. 2014. World soil Information database. URL: https://soilgrids.org/#!/?layer=ORCDRC_M_sl2_250m&vector=1 (accessed August 2018).

Jiménez, D., J. Cock, H.F. Satizábal, M.A. Barreto, A.A. Pérez-Uribe, A. Jarvis, and P. Van Damme. 2009. Analysis of Andean blackberry (Rubus glaucus) production models obtained by means of artificial neural networks exploiting information collected by small-scale growers in Colombia and publicly available meteorological data. Comput. Electron. Agric. 69, 198-208. Doi: 10.1016/j.compag.2009.08.008

Jiménez, D., J. Cock, A. Jarvis, J. Garcia, H.F. Satizábal, P. Van Damme, A. Pérez-Uribe, and M.A. Barreto-Sanz. 2011. Interpretation of commercial production information: a case study of lulo (Solanum quitoense), an under-researched Andean fruit. Agric. Syst. 104, 258-270. Doi: 10.1016/j.agsy.2010.10.004

Julia, C. and M. Dingkuhn. 2013. Predicting temperature induced sterility of rice spikelets requires simulation of crop-generated microclimate. Eur. J. Agron. 49, 50-60. Doi: 10.1016/j.eja.2013.03.006

Khoury, C.K., A.D. Bjorkman, H. Dempewolf, J. Ramirez-Villegas, L. Guarino, A. Jarvis, L.H. Rieseberg, and P.C. Struik. 2014. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl. Acad. Sci. U.S.A. 111, 4001-6. Doi: 10.1073/pnas.1313490111

Kumbhakar, S.C. and K. Lovell. 2000. Stochastic frontier analysis. Cambridge Univeristy Press, Cambridge, UK. Doi: 10.1111/1467-8276.t01-1-00317

Licker, R., M. Johnston, J.A. Foley, C. Barford, C.J. Kucharik, C. Monfreda, and N. Ramankutty. 2010. Mind the gap: how do climate and agricultural management explain the “yield gap” of croplands around the world? Glob. Ecol. Biogeogr. 19, 769-782. Doi: 10.1111/j.1466-8238.2010.00563.x

Lipper, L., P. Thornton, B.M. Campbell, T. Baedeker, A. Braimoh, M. Bwalya, P. Caron, A. Cattaneo, D. Garrity, K. Henry, R. Hottle, L. Jackson, A. Jarvis, F. Kossam, W. Mann, N. McCarthy, A. Meybeck, H. Neufeldt, T. Remington, P.T. Sen, R. Sessa, R. Shula, A. Tibu, and E.F. Torquebiau. 2014. Climate-smart agriculture for food security. Nat. Clim. Chang. 4, 1068-1072. Doi: 10.1038/nclimate2437

Lobell, D.B., K.G. Cassman, and C.B. Field. 2009. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, 179-204. Doi: 10.1146/annurev.environ.041008.093740

Longstreth, D.J. and P.S. Nobel. 1980. Nutrient influences on leaf photosynthesis. Plant Physiol. 65, 541-543. Doi: 10.1104/pp.65.3.541

Llanos, L. and D. Arango. 2015. RClimTool: a free application for analyzing climatic series. Working Paper International Center for Tropical Agriculture. URL: https://cgspace.cgiar.org/handle/10568/63482 (accessed August 2018).

MADR. 2012. Anuario estadístico del sector agropecuario 2012. URL: https://www.agronet.gov.co/Noticias/Paginas/Noticia842.aspx (accessed July 2018).

Mueller, N.D., J.S. Gerber, M. Johnston, D.K. Ray, N. Ramankutty, and J.A. Foley. 2012. Closing yield gaps through nutrient and water management. Nature 490, 254-257. Doi: 10.1038/nature11420

Mythili, G. and K.R. Shanmugam. 2000. Technical efficiency of rice growers in Tamil Nadu: a study based on panel data. Indian J. Agric. Econ. 55, 15-25. Doi: 10.22004/ag.econ.297715

Nagai, T. and A. Makino. 2009. Differences between rice and wheat in temperature responses of photosynthesis and plant growth. Plant Cell Physiol. 50, 744-55. Doi: 10.1093/pcp/pcp029

Pardo, C.E. and C. Del Campo. 2007. Combinación de métodos factoriales y de análisis de conglomerados en R: el paquete FactoClass. Rev. Colomb. Estad. 30, 231-245.

Peng, S., J. Huang, J.E. Sheehy, R.C. Laza, R.M. Visperas, X. Zhong, G.S. Centeno, G.S. Khush, and K.G. Cassman. 2004. Rice yields decline with higher night temperature from global warming. Proc. Natl. Acad. Sci. U.S.A. 101, 9971-9975. Doi: 10.1073/pnas.0403720101

Perdomo, J. and J. Mendieta. 2007. Factores que afectan la eficiencia técnica y asignativa en el sector cafetero colombiano: una aplicación con análisis envolvente de datos. Desarrollo y Sociedad 2007-II, 1-45.

Perdomo, J. and D. Hueth. 2011. Funciones de producción y eficiencia técnica en el eje cafetero colombiano: una aproximación con frontera estocástica. Rev. Colomb. Estad. 34, 377-402. Doi: 10.22004/ag.econ.100873

R Core Team. 2017. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Tian, W. and G.H. Wan. 2000. Technical Efficiency and Its Determinants in China’s Grain Production. J. Product. Anal. 13, 159-174. Doi: 10.1023/A:1007805015716

Tilman, D. and M. Clark. 2014. Global diets link environmental sustainability and human health. Nature 515, 518-522. Doi: 10.1038/nature13959

UN. 2010. World Population Prospects: The 2010 Revision URL: https://www.oecd-ilibrary.org/content/publication/08b807d4-en (accessed July 2018).

Van Bussel, L.G.J., P. Grassini, J. Van Wart, J. Wolf, L. Claessens, H. Yang, H. Boogaard, H. de Groot, K. Saito, K.G. Cassman, and M.K. van Ittersum. 2015. From field to atlas: upscaling of location-specific yield gap estimates. F. Crop. Res. 177, 98-108. Doi: 10.1016/j.fcr.2015.03.005

Van Wart, J., L.G.J. van Bussel, J. Wolf, R. Licker, P. Grassini, A. Nelson, H. Boogaard, J. Gerber, N.D. Mueller, L. Claessens, M.K. van Ittersum, and K.G. Cassman. 2013. Use of agro-climatic zones to upscale simulated crop yield potential. F. Crop. Res. 143, 44-55. Doi: 10.1016/j.fcr.2012.11.023

Villano, R. and E. Fleming. 2004. Analysis of technical efficiency in rainfed lowland rice environment in Central Luzon Philippines using stochastic frontier production function with heteroskedastic error structure. Working paper series in Agricultural and Resource Economics. University of New England, Armidale, Australia. Doi: 10.22004/ag.econ.12906

West, P.C., J.S. Gerber, P.M. Engstrom, N.D. Mueller, K.A. Brauman, K.M. Carlson, E.S. Cassidy, M. Johnston, G.K. MacDonald, D.K. Ray, and S. Siebert. 2014. Leverage points for improving global food security and the environment. Science 345, 325-328. Doi: 10.1126/science.1246067

Wheeler, T. and J. von Braun. 2013. Climate change impacts on global food security. Science 341(6145), 508-513. Doi: 10.1126/science.1239402

Xua, X. and S.R. Jeffrey. 1998. Efficiency and technical progress in traditional and modern agriculture: evidence from rice production in China. Agric. Econ. 18, 157-165. Doi: 10.1111/j.1574-0862.1998.tb00495.x

How to Cite

APA

Arango-Londoño, D., Ramírez-Villegas, J., Barrios-Pérez, C., Bonilla-Findji, O., Jarvis, A. and Uribe, J. M. (2020). Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis. Agronomía Colombiana, 38(1), 110–119. https://doi.org/10.15446/agron.colomb.v38n1.79470

ACM

[1]
Arango-Londoño, D., Ramírez-Villegas, J., Barrios-Pérez, C., Bonilla-Findji, O., Jarvis, A. and Uribe, J.M. 2020. Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis. Agronomía Colombiana. 38, 1 (Jan. 2020), 110–119. DOI:https://doi.org/10.15446/agron.colomb.v38n1.79470.

ACS

(1)
Arango-Londoño, D.; Ramírez-Villegas, J.; Barrios-Pérez, C.; Bonilla-Findji, O.; Jarvis, A.; Uribe, J. M. Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis. Agron. Colomb. 2020, 38, 110-119.

ABNT

ARANGO-LONDOÑO, D.; RAMÍREZ-VILLEGAS, J.; BARRIOS-PÉREZ, C.; BONILLA-FINDJI, O.; JARVIS, A.; URIBE, J. M. Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis. Agronomía Colombiana, [S. l.], v. 38, n. 1, p. 110–119, 2020. DOI: 10.15446/agron.colomb.v38n1.79470. Disponível em: https://revistas.unal.edu.co/index.php/agrocol/article/view/79470. Acesso em: 28 mar. 2024.

Chicago

Arango-Londoño, David, Julián Ramírez-Villegas, Camilo Barrios-Pérez, Osana Bonilla-Findji, Andy Jarvis, and Jorge Mario Uribe. 2020. “Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis”. Agronomía Colombiana 38 (1):110-19. https://doi.org/10.15446/agron.colomb.v38n1.79470.

Harvard

Arango-Londoño, D., Ramírez-Villegas, J., Barrios-Pérez, C., Bonilla-Findji, O., Jarvis, A. and Uribe, J. M. (2020) “Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis”, Agronomía Colombiana, 38(1), pp. 110–119. doi: 10.15446/agron.colomb.v38n1.79470.

IEEE

[1]
D. Arango-Londoño, J. Ramírez-Villegas, C. Barrios-Pérez, O. Bonilla-Findji, A. Jarvis, and J. M. Uribe, “Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis”, Agron. Colomb., vol. 38, no. 1, pp. 110–119, Jan. 2020.

MLA

Arango-Londoño, D., J. Ramírez-Villegas, C. Barrios-Pérez, O. Bonilla-Findji, A. Jarvis, and J. M. Uribe. “Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis”. Agronomía Colombiana, vol. 38, no. 1, Jan. 2020, pp. 110-9, doi:10.15446/agron.colomb.v38n1.79470.

Turabian

Arango-Londoño, David, Julián Ramírez-Villegas, Camilo Barrios-Pérez, Osana Bonilla-Findji, Andy Jarvis, and Jorge Mario Uribe. “Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis”. Agronomía Colombiana 38, no. 1 (January 1, 2020): 110–119. Accessed March 28, 2024. https://revistas.unal.edu.co/index.php/agrocol/article/view/79470.

Vancouver

1.
Arango-Londoño D, Ramírez-Villegas J, Barrios-Pérez C, Bonilla-Findji O, Jarvis A, Uribe JM. Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis. Agron. Colomb. [Internet]. 2020 Jan. 1 [cited 2024 Mar. 28];38(1):110-9. Available from: https://revistas.unal.edu.co/index.php/agrocol/article/view/79470

Download Citation

CrossRef Cited-by

CrossRef citations1

1. Soenke Ziesche, Swati Agarwal, Uday Nagaraju, Edson Prestes, Naman Singha. (2023). The Ethics of Artificial Intelligence for the Sustainable Development Goals. Philosophical Studies Series. 152, p.379. https://doi.org/10.1007/978-3-031-21147-8_21.

Dimensions

PlumX

Article abstract page views

1024

Downloads

Download data is not yet available.