Original Articles Open access

Cropping pattern simulation-optimization model for water use efficiency and economic return

Authors

Published
23 December 2021
Section
Original Articles
DOI
10.4081/jae.2021.1197

Abstract

Sustainable agricultural development is one of the most important tools for the economic growth of a country. Therefore, water and land use management is considered a priority. This research aimed to develop a framework to optimize crops’ spatial and temporal distribution in an irrigation district. The AquaCrop- OS (FAO) water productivity model was integrated with a nonlinear optimization model to maximize the annual net profitability and minimize the water consumption of three crops (rice, corn, and forage). It was applied at a regional level to 905 simulation sub-units in the Zulia irrigation district (Colombia), in three typical climatic years’ scenarios, and at a multi-period level (monthly). The results indicated that: i) crop simulation for the study area was applicable and feasible; ii) rice can be combined with forage and corn; iii) corn is a viable option under dry year conditions; iv) under a wet year, forage production is the best option. On average, in the dry year, profitability decreased by 14.5% compared to the normal year in half of the study area, and in some areas, economic losses of up to 53% were obtained. In the wet year, profitability remained at the same level as the normal year in 43.8% of the area. However, there were significant decreases in profitability in 23.1% of the district. In the normal year, the water demand of the crops in each simulated period allows savings of up to 50% of water compared to the current concession amount, which is 1000 mm. This study is useful for making decisions on sustainable resources management and optimal irrigation water and land use under different biophysical and economic conditions.

Keywords

References

  1. Allen R., Pereira L., Raes D., Smith M. 1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO 56 - Food and Agriculture Organization of the United Nations, Roma, Italy.
  2. Anselin L., Syabri I., Kho Y. 2006. GeoDa: an introduction to spatial data analysis. Geogr. Anal. 38:5-22. DOI: https://doi.org/10.1111/j.0016-7363.2005.00671.x
  3. Arthur D., Vassilvitskii S. 2007. k-means++: The advantages of careful seeding. pp 1027-35 in SODA 07, Proceedings of the Eighteenth Annual Acm-Siam Symposium on Discrete Algorithms, edited by Harold Gabow, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA.
  4. Bello Z.A., Walker S. 2016. Calibration and validation of AquaCrop for pearl millet (Pennisetum glaucum). Crop Pasture Sci. 67:948. DOI: https://doi.org/10.1071/CP15226
  5. Cortés-Bello C.A., Bernal-Patiño J.G., Díaz-Almanza E.D., Méndez-Monroy J.F. 2013. Uso del modelo AquaCrop para estimar rendimientos para el cultivo de maíz en los departamentos de Córdoba, Meta, Tolima y Valle del Cauca. FAO, Bogotá, DC, Colombia.
  6. Crusciol C.A., Momesso L., Portugal J.R., Costa C.H., Bossolani J.W., Costa N.R., Pariz C.M., Castilhos A.M., Rodrigues V.A., Costa C., Franzluebbers A.J., Cantarella H. 2021. Upland rice intercropped with forage grasses in an integrated crop-livestock system: Optimizing nitrogen management and food production. Field Crops Res. 261:108008. DOI: https://doi.org/10.1016/j.fcr.2020.108008
  7. Daghighi A., Nahvi A., Kim U. 2017. Optimal cultivation pattern to increase revenue and reduce water use: application of linear programming to arjan plain in fars province. Agriculture. 7:73. DOI: https://doi.org/10.3390/agriculture7090073
  8. DANE. National Administrative Department of Statistics of Colombia. 2017. National survey of household budgets - ENPH.
  9. Doorenbos J., Kassam A. 1979. Yield response to water. Food and Agriculture Organization, Rome, Italy. DOI: https://doi.org/10.1016/B978-0-08-025675-7.50021-2
  10. Fan Y., He L., Kang S., Wang S., Fang Y. 2021. A novel approach to dynamically optimize the spatio-temporal distribution of crop water consumption. J. Clean. Prod. 310:127439. DOI: https://doi.org/10.1016/j.jclepro.2021.127439
  11. Foster T., Brozović N., Butler A.P., Neale C.M.U., Raes D., Steduto P., Fereres E., Hsiao T.C. 2017. AquaCrop-OS: an open-source version of FAO’s crop water productivity model. Agric. Water Manag. 181:18-22. DOI: https://doi.org/10.1016/j.agwat.2016.11.015
  12. Hadebe S.T., Modi A.T., Mabhaudhi T. 2017. Calibration and testing of AquaCrop for selected sorghum genotypes. Water SA. 43:209. DOI: https://doi.org/10.4314/wsa.v43i2.05
  13. Hart W.E., Laird C.D., Watson J., Woodruff D.L., Hackebeil G.A., Nicholson B.L., Siirola J.D. 2017. Pyomo - Optimization modeling in Python. 2nd rev. ed. Springer International Publishing, Boston, MA, USA. DOI: https://doi.org/10.1007/978-3-319-58821-6
  14. He L., Bao J., Daccache A., Wang S., Guo P. 2020. Optimize the spatial distribution of crop water consumption based on a cellular automata model: a case study of the middle Heihe River basin, China. Sci. Total Environ. 720:137569. DOI: https://doi.org/10.1016/j.scitotenv.2020.137569
  15. Hoon M.D., Imoto S., Miyano S. 2017. The C clustering library. The University of Tokyo, Institute of Medical Science, Human Genome Center, Tokyo, Japan.
  16. IDEAM. Instituto de Hidrología, Meteorología y Estudios Ambientales. 2018. Estudio nacional del agua. Bogotá, DC, Colombia.
  17. Karki S., Rizal G., Quick W.P. 2013. Improvement of photosynthesis in rice (Oryza sativa L.) by inserting the C4 pathway. Rice 6:1. DOI: https://doi.org/10.1186/1939-8433-6-28
  18. Kuschel-Otárola M., Rivera D., Holzapfel E., Palma C., Godoy-Faúndez A. 2018. Multiperiod optimisation of irrigated crops under different conditions of water availability. Water 10:1434. DOI: https://doi.org/10.3390/w10101434
  19. Lorite I.J., García-Vila M., Santos C., Ruiz-Ramos M., Fereres E. 2013. AquaData and AquaGIS: two computer utilities for temporal and spatial simulations of water-limited yield with AquaCrop. Comput. Electron. Agric. 96:227-37. DOI: https://doi.org/10.1016/j.compag.2013.05.010
  20. Li J., Jiao X., Jiang H., Song J., Chen L. 2020. Optimization of Irrigation Scheduling for Maize in an Arid Oasis Based on Simulation–Optimization Model. Agron.10: 935. DOI: https://doi.org/10.3390/agronomy10070935
  21. Liu X., Guo P., Li F., Zheng W. 2019. Optimization of planning structure in irrigated district considering water footprint under uncertainty. J. Clean. Prod. 210:1270-80. DOI: https://doi.org/10.1016/j.jclepro.2018.10.339
  22. Mardani M., Ziaei S., Nikouei A. 2019. Optimal cropping pattern modifications with the aim of environmental-economic decision making under uncertainty. IJAMAD 8:365-75.
  23. MathWorks. 2018. Parallel computing toolbox: user’s guide; technical report. The MathWorks, Inc.: Natick, MA, USA.
  24. Mohammadzadeh A., Vafabakhsh J., Mahdavi Damghani A., Deihimfard R. 2020. Optimal land allocation to crop production in different decision priorities and water availability scenarios: East Azerbaijan province of Iran. Arch. Agron. Soil Sci. 1-18. DOI: https://doi.org/10.1080/03650340.2020.1843637
  25. Moraes A., Alexandre C., Crusciol C., Lang C., Pariz C., Deiss L., Sulc R., De Faccio Carvalho P. 2019. Integrated crop-livestock systems as a solution facing the destruction of pampa and cerrado biomes in South America by intensive monoculture systems. Academic Press, New York, NY, USA. DOI: https://doi.org/10.1016/B978-0-12-811050-8.00016-9
  26. Ouyang Z., Zheng H., Xiao Y., Polasky S., Liu J., Xu W., Wang Q., Zhang L., Xiao Y., Rao E., Jiang L., Lu F., Wang X., Yang G., Gong S., Wu B., Zeng Y., Yang W., Daily G.C. 2016. Improvements in ecosystem services from investments in natural capital. Sci. 352:1455-9. DOI: https://doi.org/10.1126/science.aaf2295
  27. Pahmeyer C., Kuhn T., Britz W. 2021. ‘Fruchtfolge’: A crop rotation decision support system for optimizing cropping choices with big data and spatially explicit modeling. Comput. Electron. Agric.181:105948. DOI: https://doi.org/10.1016/j.compag.2020.105948
  28. Pariz C.M., Costa C., Crusciol C.A., Castilhos A.M., Meirelles P.R., Roça R.O., Pinheiro R.S., Kuwahara F.A., Martello J.M., Cavasano F.A., Yasuoka J.I., Sarto J.R., Melo V.F., Franzluebbers A.J. 2017. Lamb production responses to grass grazing in a companion crop system with corn silage and oversowing of yellow oat in a tropical region. Agric. Syst. 151:1-11. DOI: https://doi.org/10.1016/j.agsy.2016.11.004
  29. Pei W., Guo X., Ren Y., Liu H. 2021. Study on the optimization of staple crops spatial distribution in China under the influence of natural disasters. J. Clean. Prod. 278:123548. DOI: https://doi.org/10.1016/j.jclepro.2020.123548
  30. Rădulescu C.Ţ.Z., Rădulescu M. 2012. A decision support tool based on a portfolio selection model for crop planning under risk. Stud. Inform. Control. 21:377-82. DOI: https://doi.org/10.24846/v21i4y201203
  31. Raes D., Steduto P., Hsiao T. C., Fereres E. 2009. AquaCrop-The FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron. J. 101:438-47. DOI: https://doi.org/10.2134/agronj2008.0140s
  32. Ruane A.C., Goldberg R., Chryssanthacopoulos J. 2015. Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agric. Forest Meteorol. 200:233-48. DOI: https://doi.org/10.1016/j.agrformet.2014.09.016
  33. Steduto P., Hsiao T.C., Raes D., Fereres E. 2009. AquaCrop-The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J. 101:426-37. DOI: https://doi.org/10.2134/agronj2008.0139s
  34. Sylvester J., Valencia J., Verchot L.V., Chirinda N., Romero Sanchez M.A., Quintero M., Castro-Nunez A. 2020. A rapid approach for informing the prioritization of degraded agricultural lands for ecological recovery: A case study for Colombia. J. Nat. Conserv. 58:125921. DOI: https://doi.org/10.1016/j.jnc.2020.125921
  35. Tan M., Zheng L. 2019. Increase in economic efficiency of water use caused by crop structure adjustment in arid areas. J. Environ. Manage. 230:386-91. DOI: https://doi.org/10.1016/j.jenvman.2018.09.060
  36. Terán-Chaves C.A. 2015. Determinación de la huella hídrica y modelación de la producción de biomasa de cultivos forrajeros a partir del agua en la Sabana de Bogotá (Colombia). Degree Diss. Universitat Politècnica de València, España.
  37. World Bank. 2020. Agriculture, forestry, and fishing, value added (constant 2010 US$) - Colombia. Available from: https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS
  38. Yeşilköy S., Şaylan L. 2020. Assessment and modelling of crop yield and water footprint of winter wheat by AquaCropItal. J. Agrometeorol. 1:3-14.
  39. Zeleke K.T. 2019. AquaCrop calibration and validation for Faba Bean (Vicia faba L.) under different agronomic managements. Agron. 9:320. DOI: https://doi.org/10.3390/agronomy9060320
  40. Zhou L., Liang S., Ponce K., Marundon S., Ye G., Zhao X. 2015. Factors affecting head rice yield and chalkiness in indica rice. Field Crops Res. 172:1-10. DOI: https://doi.org/10.1016/j.fcr.2014.12.004

Metrics

Citations and online attention from Dimensions, Altmetric and PlumX.

PlumX Metrics

License

Copyright (c) 2021 the Author(s)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.