JWARP  Vol.6 No.13 , September 2014
Climate Based Risk Assessment for Maize Producing Areas in Rainfed Agriculture in Mexico
Abstract: Rainfed areas in Mexico accounts for 14 million hectares where around 23 million people live and are located in places where there is a little climatic information. The severe drought that has impacted northern Mexico in the past several years as well as other parts of the country, has forced decision takers to look for improved tools and procedures to prevent and to cope with this natural hazard. For this paper, the methodology of the Food and Agricultural Organization of the United Nations (FAO) for estimating water balance variables was modified to provide crop yield estimations under rainfed agriculture in maize producer states of Mexico. The water balance accounts for the daily variation of soil water content having main input rainfall (Pp) and main output crop evapotranspiration (Eta). The algorithm computes crop yield using two distinctive approaches: 1) one based on surplus/deficit functions for each crop considered and 2) yield estimations based on soil water balance and water function productions of the crop being analyzed. For computing water balance and crop yields, a computer model is built that incorporates the FAO method for water balance (MODEL SICTOD: Computational System for Decision Taking, acronym in Spanish) which stochastically generate precipitation based on wet/dry transition probabilities using a first order Markov chain scheme. Maps of average crop yields were obtained after interpolating model outcomes for the main maize producer states of Mexico: Jalisco, Michoacan, Guerrero, Puebla Oaxaca and Chiapas. Different planting dates were analyzed, early (90 days of length period), intermediate (120 days of length period) and late (150 days of length period). Crop yield variability correlates to the transition probability on having a wet day following a dry day. Results have shown high yield variation and probability of crop yield failure and climatic risk follows a distinctive pattern according to planting date and rainfall occurrence. The approach used is of great support for decision taking processes.
Cite this paper: Cohen, I. , Arriaga, G. , Valle, M. , Ibarra, M. , Villalobos, A. and Hurtado, P. (2014) Climate Based Risk Assessment for Maize Producing Areas in Rainfed Agriculture in Mexico. Journal of Water Resource and Protection, 6, 1228-1237. doi: 10.4236/jwarp.2014.613112.

[1]   Sivakumar, M.V.K., Motha, R.P., Wilhite, D.A. and Wood, D.A. (2011) Agricultural Drought Indices. Proceedings of the WMO/UNISDR Expert Group Meeting on Agricultural Drought Indices, 2-4 June 2010, Murcia, Spain: Geneva, Switzerland: World Meteorological Organization. AGM-11, WMO/TD No. 1572; WAOB-2011, 197.

[2]   Cohen I.S., Spring, U.O., Padilla, G.D., Paredes, J.C., Inzunza, M.A., LopezLopez, R. and Vi-llanueva Diaz, J. (2012) Forced Migration, Climate Change, Mitigation and Adaptative Policies in Mexico: Some Functional Relationships. International Migration, 51, 53-57.

[3]   Brisson N., Seguin, B. and Bertuzzi, P. (1992) Agrometeorological Soil Water Balance for Crop Simulation Models. Agricultural and Forest Meteorology, 59, 267-287.

[4]   Panigrahi, B. and Panda, S.N. (2003) Field Test of a Soil Water Balance Simulation Model. Agricultural Water Management, 588, 223-240.

[5]   Leuven, K.U. (2002) BUDGET: A Soil Water and Salt Balance Model. Reference Manual. Faculty of Agricultural and Applied Biological Sciences, Institue for Land and Water Management, Vital Decosterstraat 102, B-3000 Leuven, Belgium.

[6]   Bargaoui, Z.K. (2012) Estimation of evapotranspiration Using Soil Water Balance Modelling, Evapotranspiration—Remote Sensing and Modeling, Dr. AyseImark (Ed.), ISBN: 978-953-307-808-3, In Tech.

[7]   Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1988) Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56, Rome, 300.

[8]   Doorembos, J. and Kassam A.H. (1979) Yield Response to Water. FAO Irrigation and Drainage Paper No. 33, FAO, Rome, 193.

[9]   Bootsma, A., Boisvert, J. and Dumanski, J. (1994) Climate-Based Estimates of Potential Forage Yields in Canada Using a Crop Growth Model. Agricultural and Forest Meteorology, 67, 151-172.

[10]   Hutchinson, M.F. (1990) Climatic Analysis in Data Sparse Regions. Proceedings of the International Symposium on Climate Risk in Crop Production: Models and Management for the Semi-Arid Tropics and Subtropics, Brisbane, 2-6 July 1990, 55-71.

[11]   JRC Scientific and Technical Reports (2007) CGMS Version 9.2. User Manual and Technical Documentation. Luxemburg.

[12]   Hanson, C.L., Neff, E.L. and Woolhiser, D.A. (1975) Hydrologic Aspects of Water Harvesting in the Northern Great Planes. Proc., Water Harvesting Symp., ARS W-22, US Department of Agriculture-Agricultural Research Service, Water Conservation Laboratory, Phoenx, 129-140.

[13]   Sanchez Cohen, I., Lopes, V.L., Slack, D.C. and Fogel, M.M. (1997) Water Balance Model for Small-Scale Water Harvesting Systems. Journal of Irrigation and Drainage Engineering, 123, 123-128.

[14]   Woolhiser, D.A. and Roldan, J. (1986) Seasonal and Regional Variability of Parameters for Stochastic Daily Precipitation Models: South Dakota, U.S.A. Water Resources Research, 22, 965-978.

[15]   Wilks, D.S. (1995) Statistical Methods in the Atmospheric Sciences. Academic Press, San Diego, 467 p.

[16]   Ikbadun, H.E., Tarimo, A.K.P.R., Salim, B.A. and Mahoo, H.F. (2007) Evaluation of Selected Crop Water Production Functions for an Irrigated Maize Crop. Agricultural Water Management, 94, 1-10.

[17]   Brumbelow, K. and Georgakakos, A. (2007) Determining Crop-Water Production Functions Using Yield-Irrigation Gradient Algorithms. Agricultural Water Management, 87, 151-161.

[18]   OECD (2011) Managing Risk in Agriculture: Policy Assessment and Design. OECD Publishing.