Strategies and technologies of cereal sector in the face of climatic variability. A literatura review

Silvia Soledad Moreno Gutierrez, Alfredo Toriz Palacios, Sócrates López Pérez, Abraham Sánchez López


At the international level, cereals are considered important foods for economic development; corn, wheat and rice represent some of the crops with the highest consumption and production, so their participation in aspects of food security is also decisive; nevertheless, the cereal sector faces one of the greatest current challenges, Climate Change, a phenomenon that has caused food insecurity due to the loss of production, yield and quality in crops; a situation that by the year 2050, if the adaptation and mitigation are not achieved, according to global forecasts will bring greater consequences. Therefore, the objective of this paper is to review the literature that offers a global overview of the trends related to the strategies and technologies implemented in the sector, which strengthen their competitiveness and contribute to maintaining profitability.


Cereal sector, strategies, technologies, profitability, competitiveness

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Al‐Mahasneh, M., Alkoaik, F., Khalil, A., Al‐Mahasneh, A., El‐Waziry, A., Fulleros, R., & Rababah, T. (2014). A generic method for determining moisture sorption isotherms of cereal grains and legumes using artificial neural networks. Journal of Food Process Engineering, 37(3), 308-316.

Arkeman, Y., B Seminar, K., & J Lahay, R. (2014). An Intelligent System For Early Detection of Food Crisis And Spatial-Based Decision Making of Potential Land Evaluation For Food Production.

Barrero, O., Rojas, D., Gonzalez, C., & Perdomo, S. (2016, August). Weed detection in rice fields using aerial images and neural networks. In Signal Processing, Images and Artificial Vision (STSIVA), 2016 XXI Symposium on (pp. 1-4). IEEE.

Basche, A. D., Archontoulis, S. V., Kaspar, T. C., Jaynes, D. B., Parkin, T. B., & Miguez, F. E. (2016). Simulating long-term impacts of cover crops and climate change on crop production and environmental outcomes in the Midwestern United States. Agriculture, Ecosystems & Environment, 218, 95-106.

Basche, A. D., Archontoulis, S. V., Kaspar, T. C., Jaynes, D. B., Parkin, T. B., & Miguez, F. E. (2016). Simulating long-term impacts of cover crops and climate change on crop production and environmental outcomes in the Midwestern United States. Agriculture, Ecosystems & Environment, 218, 95-106.

Beigi, M., Torki-Harchegani, M., & Mahmoodi-Eshkaftaki, M. (2016). Prediction of paddy drying kinetics: a comparative study between mathematical and artificial neural network modelling. Chemical Industry and Chemical Engineering Quarterly, (00), 39-39.

Benjamin, K. K., Casimir, K. A., Masse, D., & Emmanuel, A. N. (2015). Batch fermentation process of sorghum wort modeling by artificial neural network. European Scientific Journal, 11(3).

Bogard, M., Ravel, C., Paux, E., Bordes, J., Balfourier, F., Chapman, S., & Allard, V. (2014). Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model. Journal of experimental botany, eru328.

Bose, P., Kasabov, N. K., Bruzzone, L., & Hartono, R. N. (2016). Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series. IEEE Transactions on Geoscience and Remote Sensing, 54(11), 6563-6573.

Chantre, G. R., Blanco, A. M., Forcella, F., Van Acker, R. C., Sabbatini, M. R., & González-Andújar, J. L. (2014). A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence. The Journal of Agricultural Science, 152(02), 254-262.

Cleves, J. A., Toro, J., & Martínez, L. F. (2017). Los balances hídricos agrícolas en modelos de simulación agroclimáticos. Una revisión analítica. Revista Colombiana de Ciencias Hortícolas, 10(1), 149-163.

Conde, C. I. C. (2014). Cambio climático y agrobiodiversidad. Revista Colombiana de Investigaciones Agroindustriales, 1(1), 72-79.

Constantinescu, A. (2017). Neuro-fuzzy concepts applied for planning of the cereal crops: applications to the maize hybrids growing in a Romanian region. Journal of Biological Dynamics, 11(1), 1-7.

Donné, S., Luong, Q., Goossens, B., Dhondt, S., Wuyts, N., Inzé, D., & Philips, W. (2016). Machine learning for maize plant segmentation. In Belgian-Dutch Conference on Machine Learning (BENELEARN).

Eitzinger, J., Thaler, S., Schmid, E., Strauss, F., Ferrise, R., Moriondo, M. & Olesen, J. E. (2013). Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria. The Journal of Agricultural Science, 151(06), 813-835.

Farjam, A., Omid, M., Akram, A., & Fazel Niari, Z. (2014). A neural network based modeling and sensitivity analysis of energy inputs for predicting seed and grain corn yields. Journal of Agricultural Science and Technology, 16(4), 767-778.

Fernández Núñez, E. G., Barchi, A. C., Ito, S., Escaramboni, B., Herculano, R. D., Mayer, C. R. M., & de Oliva Neto, P. (2016). Artificial intelligence approach for high level production of amylase using Rhizopus microsporus var oligosporus and different agro‐industrial wastes. Journal of Chemical Technology and Biotechnology.

Field, C., Barros, V., Dokken, D., Mach, K., Mastrandrea, M., & Bilir, T. (2014). Cambio climático 2014: impactos, adaptación y vulnerabilidad. Quinto Informe de Evaluación (GTII IE5) del IPCC.

Galindo, L. M. (2009). La economía del cambio climático en Méxicosíntesis (No. F/363.7387 E2).

Gallo, A. (2015). Assessment of the climate change impact and adaptation strategies on Italian cereal production using high resolution climate data.

Gandhi, N., Petkar, O., & Armstrong, L. J. (2016, July). Rice crop yield prediction using artificial neural networks. In Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2016 IEEE (pp. 105-110). IEEE.

García, A., & Del Fabro, L. (2015). Experience on food purchase from family farming by Brazil´ s School Lunch Program: design determining factors. Investigación Agraria, 17(1), 3-17.

Golpour, I., Parian, J. A., & Chayjan, R. A. (2014). Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network. Czech Journal of Food Science, 32(3), 280-287.

Goyal, S. (2013). Predicting properties of cereals using artificial neural networks: A review. Scientific Journal of Crop Science, 2(7), 95-115.

Goyal, S. (2013). Predicting properties of cereals using artificial neural networks: A review. Scientific Journal of Crop Science, 2(7), 95-115.

Graß, R., Thies, B., Kersebaum, K. C., & Wachendorf, M. (2015). Simulating dry matter yield of two cropping systems with the simulation model HERMES to evaluate impact of future climate change. European Journal of Agronomy70, 1-10.

He, Z., Xia, X., Peng, S., & Lumpkin, T. A. (2014). Meeting demands for increased cereal production in China. Journal of Cereal Science, 59(3), 235-244.

Hussein, W. B., Moaty, A. A., Nagaty, K. A., & Hussein, M. A. (2016). Non-Contact Measurement of Cereal Quality by Image Sensing and Numerical Regression Techniques. In MATEC Web of Conferences (Vol. 75, p. 01002). EDP Sciences.

Íñiguez-Covarrubias, M., Ojeda-Bustamante, W., Díaz-Delgado, C., & Sifuentes-Ibarra, E. (2014). Análisis de cuatro variables del período de lluvias asociadas al cultivo maíz de temporal. Revista mexicana de ciencias agrícolas, 5(1), 101-114.

Khoshroo, A. L. I. R. E. Z. A., Arefi, A. R. M. A. N., Masoumiasl, A. S. A. D., & Jowkar, G. H. (2014). Classification of wheat cultivars using image processing and artificial neural networks. Agr Commun, 2, 17-22.

Lal, S. B., & Varma, S. P. (2014). Identification of abiotic stress related cereal proteins based on their structural composition using back propagation networks. J. Agroecol. Natural Resour. Manage, 1(4), 270-274.

Lv, H., Lei, T., Huang, X. L., & Zhang, Y. K. (2015). Application of an Improved Grey Neural Network in Grain Yield Prediction.

Mansourian, S., Darbandi, E. I., Mohassel, M. H. R., Rastgoo, M., & Kanouni, H. (2017). Comparison of artificial neural networks and logistic regression as potential methods for predicting weed populations on dryland chickpea and winter wheat fields of Kurdistan province, Iran. Crop Protection, 93, 43-51.

Mao, X., Sun, L., Hui, G., & Xu, L. (2014). Modeling research on wheat protein content measurement using near-infrared reflectance spectroscopy and optimized radial basis function neural network. Journal of food and drug analysis, 22(2), 230-235.

Matsumura, K., Gaitan, C. F., Sugimoto, K., Cannon, A. J., & Hsieh, W. W. (2015). Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. The Journal of Agricultural Science, 153(03), 399-410.

Meher, P. K., Sahu, T. K., Rao, A. R., & Wahi, S. D. (2016). Discriminating coding from non-coding regions based on codon structure and methylation-mediated substitution: An application in rice and cattle. Computers and Electronics in Agriculture, 129, 66-73.

Munguía-Aldama, J., Sánchez-Plata, F., Vizcarra-Bordi, I., & Rivas-Guevara, M. (2015). Estrategias para la producción de maíz frente a los impactos del cambio climático. Revista de Ciencias Sociales (Ve), 21(4).

Nowakowski, K., Raba, B., Tomczak, R. J., Boniecki, P., Kujawa, S., Nowak, P. J., & Matz, R. (2013, July). Identification of physical parameters of cereal grain using computer image analysis and neural models. In Fifth International Conference on Digital Image Processing (pp. 887823-887823). International Society for Optics and Photonics.

Pazoki, A., Pazoki, Z., & Sorkhilalehloo, B. (2013). Rain Fed Barley Seed Cultivars Identification Using Neural Network and Different Neurons Number. World Applied Sciences Journal, 22(5), 755-762.

Prabhu, A. A., & Jayadeep, A. (2016). Optimization of enzyme-assisted improvement of polyphenols and free radical scavenging activity in red rice bran: A statistical and neural network-based approach. Preparative Biochemistry and Biotechnology, 1-9.

Qiongyan, L., Cai, J., Berger, B., & Miklavcic, S. (2014, December). Study on spike detection of cereal plants. In Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on (pp. 228-233). IEEE.

Ravari, S. Z., Dehghani, H., & Naghavi, H. (2016). Assessment of salinity indices to identify Iranian wheat varieties using an artificial neural network. Annals of Applied Biology, 168(2), 185-194.

Reeves, T. G., Thomas, G., & Ramsay, G. (2016). Ahorrar para crecer en la práctica: maíz, arroz, trigo. Guía para la producción sostenible de cereales.

Reynolds, M. P., Quilligan, E., Aggarwal, P. K., Bansal, K. C., Cavalieri, A. J., Chapman, S. C. & Jagadish, K. S. (2016). An integrated approach to maintaining cereal productivity under climate change. Global Food Security, 8, 9-18.

Roncancio, S. S. S., Muñoz, J. G. C., & Sánchez, F. R. (2015). Estrategias de adaptación al cambio climático en dos localidades del municipio de Junín, Cundinamarca, Colombia. Revista de Investigación Agraria y Ambiental, 6(1), 227-237.

Salazar-Martínez, J., Rivera-Figueroa, C. H., Arévalo-Gallegos, S., Guevara-Escobar, A., Malda-Barrera, G., & Rascón-Cruz, Q. (2015). Calidad del nixtamal y su relación con el ambiente de cultivo del maíz. Revista fitotecnia mexicana, 38(1), 67-73.

Sapkota, T. B., Jat, M. L., Aryal, J. P., Jat, R. K., & Khatri-Chhetri, A. (2015). Climate change adaptation, greenhouse gas mitigation and economic profitability of conservation agriculture: Some examples from cereal systems of Indo-Gangetic Plains. Journal of Integrative Agriculture, 14(8), 1524-1533.

Serio, L. A. (2015). Desarrollo y validación de un modelo del sistema suelo-planta-atmósfera para la estimación de la evapotranspiración real del cultivo de maíz (Doctoral dissertation, Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires).

Sharifi, H., Hijmans, R. J., Hill, J. E., & Linquist, B. A. (2016). Using Stage-Dependent Temperature Parameters to Improve Phenological Model Prediction Accuracy in Rice Models. Crop Science.

Shi, W., Jiao, K., Liang, Y., & Wang, F. (2016). Efficient detection of internal infestation in wheat based on biophotonics. Journal of Photochemistry and Photobiology B: Biology, 155, 137-143.

Silva, C. S., & Sonnadara, U. (2013, September). Classification of rice grains using neural networks. In Proceedings of Technical Sessions (Vol. 29, pp. 9-14).

Tapia, L., Hermes Aramendiz, T., Pacheco, J., & Montalvo, A. (2015). Clusters agrícolas: un estado del arte para los estudios de competitividad en el campo. Revista de Ciencias Agrícolas, 32(2), 113-124.

Tripathi, R. C., Kalyani, V. K., Ram, L. C., & Jha, S. K. (2015). Prediction of Wheat Yield from Pond Ash Amended Field by Artificial Neural Networks. Journal of Hazardous, Toxic, and Radioactive Waste, 19(4), 04015001.

Valdez-Torres, J. B., Soto-Landeros, F., Osuna-Enciso, T., & Báez-Sañudo, M. A. (2012). Modelos de predicción fenológica para maíz blanco (Zea mays L.) y gusano cogollero (Spodoptera frugiperda JE Smith). Agrociencia, 46(4), 399-410.

Wallach, D., Nissanka, S. P., Karunaratne, A. S., Weerakoon, W. M. W., Thorburn, P. J., Boote, K. J., & Jones, J. W. (2016). Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case

Yang, J., Gong, W., Shi, S., Du, L., Sun, J., & Song, S. L. (2016). Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice. Plant Soil Environ, 62, 178-183.

Ye, L., Xiong, W., Li, Z., Yang, P., Wu, W., Yang, G. & Tang, H. (2013). Climate change impact on China food security in 2050. Agronomy for Sustainable Development, 33(2), 363-374.


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