Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops

Chapman R., Cock J., Samson M., Janetski N., Janetski K., Gusyana D., Dutta S., Oberthür T.

Data Analysis Consultant, 9 Kia Ora Parade, Ferntree Gully, VIC 3156, Australia; Emeritus, Centro Internacional de Agricultura Tropical (CIAT), Cali, Colombia; Scientific Data Management Consultant, Los Banos, Philippines; Senior Technical Advisor to the Cocoa Care Program of Community Solutions International, Makassar, Sulawesi, Indonesia; CEO of Community Solutions International, Bali, Indonesia; Lautan Luas TBK, Jakarta, Indonesia; African Plant Nutrition Institute, Lot 660, Hay Moulay Rachid, Ben Guerir, Morocco; Business and Partnership Development, African Plant Nutrition Institute, Lot 660, Hay Moulay Rachid, Ben Guerir, Morocco


Although weather is a major driver of crop yield, many farmers don’t know in advance how the weather will vary nor how their crops will respond. We hypothesized that where El Niño-Southern Oscillation (ENSO) drives weather patterns, and data on crop response to distinct management practices exists, it should be possible to map ENSO Oceanic Index (ENSO OI) patterns to crop management responses without precise weather data. Time series data on cacao farm yields in Sulawesi, Indonesia, with and without fertilizer, were used to provide proof-of-concept. A machine learning approach associated 75% of cacao yield variation with the ENSO patterns up to 8 and 24 months before harvest and predicted when fertilizer applications would be worthwhile. Thus, it’s possible to relate average cacao crop performance and management response directly to ENSO patterns without weather data provided: (1) site specific data exist on crop performance over time with distinct management practices; and (2) the weather patterns are driven by ENSO OI. We believe that the principles established here can readily be applied to other crops, particularly when there’s little data available on crop responses to management and weather. However, specific models will be required for each crop and every recommendation domain. © 2021, The Author(s).


Scientific Reports

Publisher: Nature Research

Volume 11, Issue 1, Art No 8292, Page – , Page Count

Journal Link:

doi: 10.1038/s41598-021-87520-4

Issn: 20452322

Type: All Open Access, Gold, Green


Tilman, D., Balzer, C., Hill, J., Befort, B.L., Global food demand and the sustainable intensification of agriculture (2011) Proc. Natl. Acad. Sci. U. S. A.; Bodirsky, B.L., Global food demand scenarios for the 21st century (2015) PLoS ONE; Wani, S., Rockstrom, J., Oweis, T., (2009) Rainfed Agriculture: Unlocking the Potential, , CABI; de Fraiture, C., Karlberg, L., Rockström, J., Can rainfed agriculture feed the world? An assessment of potentials and risk (2009) Rainfed Agriculture: Unlocking the Potential, ,; Chand, S.S., Chambers, L.E., Waiwai, M., Malsale, P., Thompson, E., Indigenous knowledge for environmental prediction in the Pacific Island countries (2014) Weather Clim. Soc.; Naylor, R.L., Battisti, D.S., Vimont, D.J., Falcon, W.P., Burke, M.B., Assessing risks of climate variability and climate change for Indonesian rice agriculture (2007) Proc. Natl. Acad. Sci. U. S. A., 104, pp. 7752-7757. , COI: 1:CAS:528:DC%2BD2sXmtFCitr8%3D; Keil, A., Zeller, M., Wida, A., Sanim, B., Birner, R., What determines farmers’ resilience towards ENSO-related drought? An empirical assessment in Central Sulawesi, Indonesia (2008) Clim. Change; Katz, R.W., Sir gilbert walker and a connection between el niño and statistics (2002) Stat. Sci.; McPhaden, M.J., Zebiak, S.E., Glantz, M.H., ENSO as an integrating concept in earth science (2006) Science; Ham, Y.-G., Kim, J.-H., Luo, J.-J., Deep learning for multi-year ENSO forecasts (2019) Nature; Jones, J.W., The DSSAT cropping system model (2003) Eur. J. Agron.; Podestá, G., Use of ENSO-related climate information in agricultural decision making in Argentina: a pilot experience (2002) Agric. Syst.; Newlands, N.K., An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty (2014) Front. Environ. Sci.; Lu, W., Atkinson, D.E., Newlands, N.K., ENSO climate risk: predicting crop yield variability and coherence using cluster-based PCA (2017) Model. Earth Syst. Environ.; Harrington, L.W., Tripp, R., (1984) Recommendation Domains [Target Groups in Farming Systems]: A Framework for On-Farm Research, , CIMMYT; Cock, J.H., Jones, P., Oberthür, T., Homologue and avocados: what will grow on my farm? (2008) Acta Hort.; RattalinoEdreira, J.I., Beyond the plot: technology extrapolation domains for scaling out agronomic science (2018) Environ. Res. Lett.; Rubiano, M.J.E., Cook, S., Rajasekharan, M., Douthwaite, B., A Bayesian method to support global out-scaling of water-efficient rice technologies from pilot project areas (2016) Water Int.; Agrawal, R., Mehta, S.C., Weather based forecasting of crop yields, pests and diseases—IASRI models (2007) J. Ind. Soc. Agric. Stat., 61, pp. 255-263; Hoffmann, M.P., Fertilizer management in smallholder cocoa farms of Indonesia under variable climate and market prices (2020) Agric. Syst., 178, p. 102759; Gateau-Rey, L., Tanner, E.V.J., Rapidel, B., Marelli, J.P., Royaert, S., Climate change could threaten cocoa production: effects of 2015–16 El Niño-related drought on cocoa agroforests in Bahia, Brazil (2018) PLoS ONE; (2019) National Oceanic and Atmospheric Administration (NOAA, ,; Bzdok, D., Altman, N., Krzywinski, M., Statistics versus machine learning (2018) Nat. Methods; Bzdok, D., Krzywinski, M., Altman, N., Machine learning: a primer (2017) Nat. Methods, 14, p. 1119. , COI: 1:CAS:528:DC%2BC2sXhvFWhtb7M; Tran, D., Edward: A library for probabilistic modeling, inference (2016) And Criticism; Tran, D., Deep probabililstic programming (2017) ICLR; Breiman, L., Random forests (2001) Mach. Learn.; Cock, J., Crop management based on field observations: case studies in sugarcane and coffee (2011) Agric. Syst.; Jiménez, D., From observation to information: data-driven understanding of on farm yield variation (2016) PLoS ONE; Chapman, R., Using Bayesian networks to predict future yield functions with data from commercial oil palm plantations: a proof of concept analysis (2018) Comput. Electron. Agric., 151, pp. 338-348; Lacy, J., Cropcheck: farmer benchmarking participatory model to improve productivity (2011) Agric. Syst.; Araya, F., Acevedo, R., Cabello, M.C., Jaramillo, C., Gonzalez, I., Toro, M., (2010) CropCheck Chile: Sistema de Extension para el Sector AgroAlimentario, , Fundación Chile en el ProgramaCropcheck; Anil, B., Tonts, M., Siddique, K., Grower groups and the transformation of agricultural research and extension in australia (2015) Agroecol. Sustain. Food Syst.; Montaner, J., (2004) Successful integration of research and extension combining private and public organizations: Lessons from Argentina, , International Crop Science Congress September 2004 Brisbane, Australia; Davis, K., Impact of farmer field schools on agricultural productivity and poverty in East Africa (2012) World Dev.; Klerkx, L., Aarts, N., Leeuwis, C., Adaptive management in agricultural innovation systems: the interactions between innovation networks and their environment (2010) Agric. Syst.; Wood, B.A., Agricultural science in the wild: a social network analysis of farmer knowledge exchange (2014) PLoS ONE; Rajalahti, R., Janssen, W., Pehu, E., (2008) Agricultural Innovation Systems: From Diagnostics Toward Operational Practices Systems, , Agriculture & Rural Development Department, World Bank

Indexed by Scopus

Leave a Comment