Abstract
Increasing crops’ yields to meet the world’s demand for food is one of the great challenges of these times. To achieve this, farmers must make the best decisions based on the resources available for them. In this paper, we propose the use of Global-best Harmony Search (GHS) to find the optimal farming practices and increase the yields according to the local climate and soil characteristics, following the principles of site-specific agriculture. We propose to build an aptitude function based on a random forest model trained on farms’ data combined with open data sources for climate and soil. The result is an optimizer that uses a data-driven approach and generates information on the optimized farming practices, allowing the farmer to harness the full potential of his land. The approach was tested on a case-study on maize in the state of Chiapas, Mexico, where the adoption of the practices suggested by our approach was estimated to increase average yield by 1.7 ton/ha, contributing to closing the yield gap. The proposal has the potential to be scaled to other locations, other response variables and other crops.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
van Ittersum, M.K., Cassman, K.G., Grassini, P., Wolf, J., Tittonell, P., Hochman, Z.: Yield gap analysis with local to global relevance—a review. Field Crops Res. 143, 4–17 (2012)
Zader, A.: Food and agriculture. In: Routledge Handbook of Environment and Society in Asia, p. 237 (2014)
Mase, A.S., Prokopy, L.S.: Unrealized potential: a review of perceptions and use of weather and climate information in agricultural decision making. Weather. Clim. Soc. 6(1), 47–61 (2014)
Tilman, D., Balzer, C., Hill, J., Befort, B.L.: Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. 108(50), 20260–20264 (2011)
Foley, J.A., et al.: Solutions for a cultivated planet. Nature 478(7369), 337 (2011)
Reid, W.V., Berkes, F., Wilbanks, T.J., Capistrano, D., et al.: Bridging Scales and Knowledge Systems: Concepts and Applications in Ecosystem Assessment. Island Press, Washington (2006)
Rasmussen, P.E., Goulding, K.W., Brown, J.R., Grace, P.R., Janzen, H.H., Körschens, M.: Long-term agroecosystem experiments: assessing agricultural sustainability and global change. Science 282(5390), 893–896 (1998)
Samberg, L.H., Gerber, J.S., Ramankutty, N., Herrero, M., West, P.C.: Subnational distribution of average farm size and smallholder contributions to global food production. Environ. Res. Lett. 11(12), 1–12 (2016). http://iopscience.iop.org/article/10.1088/1748-9326/11/12/124010/meta
De Janvry, A., Sadoulet, E., Suri, T.: Field experiments in developing country agriculture. In: Handbook of Economic Field Experiments, vol. 2, pp. 427–466. Elsevier, Amsterdam (2017)
Leeuwis, C.: Communication for Rural Innovation: Rethinking Agricultural Extension. Wiley, Hoboken (2013)
Cock, J., et al.: Crop management based on field observations: case studies in sugarcane and coffee. Agric. Syst. 104(9), 755–769 (2011)
Jiménez, D., et al.: From observation to information: data-driven understanding of on farm yield variation. PLoS One 11(3), 1–20 (2016)
Jiménez, D., et al.: Interpretation of commercial production information: a case study of lulo (Solanum quitoense), an under-researched Andean fruit. Agric. Syst. 104(3), 258–270 (2011)
Jiménez, D., et al.: 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(2), 198–208 (2009)
Delerce, S., et al.: Assessing weather-yield relationships in rice at local scale using data mining approaches. PLoS ONE 11(8), e0161620 (2016)
Strobl, C., Boulesteix, A., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC Bioinform. 11, 1–11 (2008)
Yu, T., Davis, L., Baydar, C., Roy, R.: Evolutionary Computation in Practice. Studies in Computational Intelligence. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75771-9
Ashlock, D.: Evolutionary Computation for Modeling and Optimization. Springer, Heidelberg (2005). https://doi.org/10.1007/0-387-31909-3
Yu, X., Gen, M.: Introduction to Evolutionary Algorithms. Springer, Heidelberg (2010). https://doi.org/10.1007/978-1-84996-129-5
Sastry, K., Goldberg, D., Kendall, G.: Genetic algorithms. In: Burke, E., Kendall, G. (eds.) Search Methodologies, pp. 93–117. Springer, Heidelberg (2014). https://doi.org/10.1007/978-1-4614-6940-7_4
Du, K.-L., Swamy, M.N.S.: Particle swarm optimization. In: Search and Optimization by Metaheuristics, pp. 153–173. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-41192-7
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A New Heuristic Optimization Algorithm: Harmony Search. Simulation 76(2), 60–68 (2001)
Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188(2), 1567–1579 (2007)
Omran, M.G., Mahdavi, M.: Global-best harmony search. Appl. Math. Comput. 198(2), 643–656 (2008)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Hagan, M.T., Demuth, H.B., Beale, M.H., De Jesús, O., et al.: Neural Network Design, vol. 20. Pws Pub, Boston (1996)
Breiman, L.: Statistical modeling: the two cultures. Stat. Sci. 16, 199–215 (2001)
Hothorn, T., Hornik, K., Zeileis, A.: Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15(3), 651–674 (2006)
Strobl, C., Boulesteix, A.-L., Zeileis, A., Hothorn, T.: Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform. 8, 25 (2007)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45014-9_1
Al-Jarrah, O.Y., Yoo, P.D., Muhaidatc, S., Karagiannidis, G.K., Tahaa, K.: Efficient machine learning for Big Data: a review. Big Data Res. 2(3), 87–93 (2015)
Gao, K.-Z., Suganthan, P.N., Pan, Q.-K., Chua, T.J., Cai, T.X., Chong, C.-S.: Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives. J. Intell. Manuf. 27(2), 363–374 (2016)
Kong, X., Gao, L., Ouyang, H., Li, S.: A simplified binary harmony search algorithm for large scale 0–1 knapsack problems. Expert Syst. Appl. 42(12), 5337–5355 (2015)
Hoang, D.C., Yadav, P., Kumar, R., Panda, S.K.: Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE Trans. Ind. Inform. 10(1), 774–783 (2014)
Assad, A., Deep, K.: Applications of harmony search algorithm in data mining: a survey. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds.) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol. 437, pp. 863–874. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-0451-3_77
Kuhn, M., et al.: Caret: Classification and Regression Training (2014)
Acknowledgements
The research has been supported by International Center for Tropical Agriculture (CIAT) and is based on data shared by the MASAGRO project lead by the International Maize and Wheat Improvement Center (CIMMYT), we also acknowledge for the open data shared by INIFAP and INEGI. We are especially grateful to Colin McLachlan for suggestions relating to the text in English.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Appendix 1. Dataset description
Reference | Description | Classification | Scale |
|---|---|---|---|
M1 | Total amount of nitrogen applied | Practices | Continuous |
M2 | Total amount of phosphorus applied | Practices | Continuous |
M3 | Total amount of potassium applied | Practices | Continues |
M4 | Number of mechanical weeding | Practices | Discrete |
M5 | Number of post-harvest herbicides applications | Practices | Discrete |
M6 | Number of “rastreo” | Practices | Discrete |
M7 | Number of pre-sowing herbicides applications | Practices | Discrete |
M8 | Number of fertilizations | Practices | Discrete |
M9 | Number of applications of foliar fertilizers | Practices | Discrete |
M10 | Number of applications of biofertilizants | Practices | Discrete |
M11 | Number of post-sowing herbicides applications | Practices | Discrete |
M12 | Number of applications of insecticides | Practices | Discrete |
M13 | Cultivars’ group criollo | Practices | Discrete |
M14 | Cultivars’ group Dekalb | Practices | Discrete |
M15 | Cultivars’ group others | Practices | Discrete |
M16 | Cultivars’ group P4082 W | Practices | Discrete |
M17 | No seed treatment | Practices | Discrete |
M18 | Seed treatment | Practices | Discrete |
M19 | Conservation agriculture | Practices | Discrete |
M20 | Zero or minimum tillage | Practices | Discrete |
M21 | Conventional tillage | Practices | Discrete |
S1 | Clay content | Soil | Continuous |
S2 | Silt content | Soil | Continuous |
S3 | Soil organic content | Soil | Continuous |
S4 | Cationic exchange capacity | Soil | Continuous |
S5 | Basis saturation | Soil | Continuous |
S6 | Low infiltration | Soil | Discrete |
S7 | Moderate infiltration | Soil | Discrete |
S8 | Good infiltration | Soil | Discrete |
W1 | Average minimum temperature | Weather | Continuous |
W2 | Average diurnal range | Weather | Continuous |
W3 | Accumulated solar energy | Weather | Continuous |
W4 | Frequency of days with maximum temperature above 34°C | Weather | Continuous |
W5 | Accumulated precipitation | Weather | Continuous |
W6 | Frequency of days with minimum temperature below 8°C | Weather | Continuous |
W7 | Average relative humidity | Weather | Continuous |
W8 | Standard deviation of the relative humidity | Weather | Continuous |
Y | Yield | Yield | Continuous |
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Dorado, H., Delerce, S., Jimenez, D., Cobos, C. (2018). Finding Optimal Farming Practices to Increase Crop Yield Through Global-Best Harmony Search and Predictive Models, a Data-Driven Approach. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Computational Intelligence. MICAI 2018. Lecture Notes in Computer Science(), vol 11289. Springer, Cham. https://doi.org/10.1007/978-3-030-04497-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-04497-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04496-1
Online ISBN: 978-3-030-04497-8
eBook Packages: Computer ScienceComputer Science (R0)