https://doi.org/10.4081/jae.2021.1197 Cropping pattern simulation-optimization model for water use efficiency and economic return
Published:23 December 2021
Abstract Views: 1194
PDF: 527
HTML: 70
HTML: 70
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
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.
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.
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
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.
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
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.
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
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
DANE. National Administrative Department of Statistics of Colombia. 2017. National survey of household budgets - ENPH.
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
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
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
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
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
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
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.
IDEAM. Instituto de Hidrología, Meteorología y Estudios Ambientales. 2018. Estudio nacional del agua. Bogotá, DC, Colombia.
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
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
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
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
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
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.
MathWorks. 2018. Parallel computing toolbox: user’s guide; technical report. The MathWorks, Inc.: Natick, MA, USA.
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
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
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
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
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
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
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
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
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
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
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
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
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.
World Bank. 2020. Agriculture, forestry, and fishing, value added (constant 2010 US$) - Colombia. Available from: https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS
Yeşilköy S., Şaylan L. 2020. Assessment and modelling of crop yield and water footprint of winter wheat by AquaCropItal. J. Agrometeorol. 1:3-14.
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
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
How to Cite
Terán-Chaves, C. A. . and Polo-Murcia, S. M. (2021) “Cropping pattern simulation-optimization model for water use efficiency and economic return”, Journal of Agricultural Engineering, 52(4). doi: 10.4081/jae.2021.1197.
Copyright (c) 2021 the Author(s) 
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.
Similar Articles
- Abderrahman Sghaier, Hanen Dhaou, Lassaad Jarray , Zouhair Abaab, Ahmed Sekrafi, Mohamed Ouessar , Assessment of drought stress in arid olive groves using HidroMORE model , Journal of Agricultural Engineering: Vol. 53 No. 1 (2022)
- Chiara Antinoro, Vincenzo Bagarello, Vito Ferro, Giuseppe Giordano, Massimo Iovino, Testing the shape-similarity hypothesis between particle-size distribution and water retention for Sicilian soils , Journal of Agricultural Engineering: Vol. 43 No. 3 (2012)
- Mario Pirastru, Marcello Niedda, Mirko Castellini, Effects of maquis clearing on the properties of the soil and on the near-surface hydrological processes in a semi-arid Mediterranean environment , Journal of Agricultural Engineering: Vol. 45 No. 4 (2014)
- Dario Friso, Lucia Bortolini, Influence of the trajectory angle and nozzle height from the ground on water distribution radial curve of a sprinkler , Journal of Agricultural Engineering: Vol. 43 No. 1 (2012)
- Ugo Lazzaro, Caterina Mazzitelli, Benedetto Sica, Paola Di Fiore, Nunzio Romano, Paolo Nasta, On evaluating the hypothesis of shape similarity between soil particle-size distribution and water retention function , Journal of Agricultural Engineering: Vol. 54 No. 4 (2023)
- Ameneh Sobhani, Shawkat B.M. Hassan, Giovanna Dragonetti, Raffaella Balestrini, Mauro Centritto, Antonio Coppola, Alessandro Comegna, Comparing actual transpiration fluxes as measured at leaf-scale and calculated by a physically based agro-hydrological model , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Luisa Martelloni, Christian Frasconi, Mino Sportelli, Marco Fontanelli, Michele Raffaelli, Andrea Peruzzi, Hot foam and hot water for weed control: A comparison , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Juan Ãngel Mintegui Aguirre, José Carlos Robredo Sánchez, Carlos de Gonzalo Aranoa, Pablo Huelin Rueda, Jorge Fallas, Felipe Cisneros, Pedro Cisneros, Adriana Urciuolo, Rodolfo Iturraspe, Forest use strategies in watershed management and restoration: application to three small mountain watersheds in Latin America , Journal of Agricultural Engineering: Vol. 45 No. 1 (2014)
- Martine Nyeko, Guido D'Urso, Walter W. Immerzeel, Adaptive simulation of the impact of changes in land use on water resources in the lower Aswa basin , Journal of Agricultural Engineering: Vol. 43 No. 4 (2012)
- Andrea Setti, Giulio Castelli, Lorenzo Villani, Roberto Ferrise, Elena Bresci, Modelling the impacts of water harvesting and climate change on rainfed maize yields in Senegal , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
You may also start an advanced similarity search for this article.
