GENERALISED WHOLE-FARM STOCHASTIC DYNAMIC ...

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EXECUTIVE SUMMARYGENERALISEDWHOLE-FARM STOCHASTIC DYNAMIC PROGRAMMING MODELTO OPTIMISE AGRICULTURAL WATER USEMotivationThe National Water Resources Strategy (NWRS) (DWAF, 2004) is a requirement of theNational Water Act (Act 36 of 1998) and describes how the water of South Africa will beprotected, used, developed, conserved and managed- The NWRS seeks to identifyopportunities where water can be made available for productive livelihoods and also to give thesupport and assistance needed to use the water effectively. The National Water Conservationand Demand Management Strategy forms an integral part of the NWRS- The agriculturalsectoral strategy was recently finalised and endeavours to provide a supportive and enablingframework to improve irrigation efficiency. Thus, there is a clear need to optimise water use inthe agricultural sector Strategies to increase efficiency of water use are both of short and longterm nature.A decade ago Backeberg and Oosthuizen (1995) posed the challenge to researchers to developand apply dynamic stochastic planning methods for managing water resource allocationproblems. When dealing with the allocation of water resources between competing uses,optimisation techniques are preferred because they take the opportunity cost of water intoaccount and are able to predict how farmers are to respond to certain changes in water policy.Researchers in South Africa seem to favour dynamic linear programming (DLP) to optimisewater resources allocation problems and the economic implications of alternative water policies(Van Schalkwyk and Louw, 2004; Louw, Van Schalkwyk and Groenewald 1998; Backeberg,1997; Viljoen, Symington and Botha, 1992; Mare, 1995 and Haile, Grove and Oosthuizen, 2003).Oosthuizen and Grove (2000) demonstrated, through the development of linear and non-linearoptimisation models of agricultural water use management at farm level, that the timing of watershortages significantly impacted on the economic efficiency of irrigation farming. They alsodemonstrated that the combined effect of water shortages in different crop growth stagessignificantly alter optimal water use patterns over the growing season and thus economicefficiency. Early research efforts to optimise agricultural water use within a DLP frameworkfocused on the use of production functions to estimate alternative activities for theirprogramming models, i.e. Viljoen, Symington, Botha and Du Plessis (1993). Productionfunctions aim at optimising seasonal water use and are not suited for situations where differentcrops compete for water within a season or where water supply limitations occur for a fraction of
Executive Summar\-the season (Bernardo, 1985)- Thus, the ability to optimise agricultural water use betweenmultiple crops within a DLP framework still needs to be demonstrated. Another complicatingfactor is that water is classified as a risk reducing input, and by implication lower waterapplication levels will therefore increase production risk (Willis, 1993). Due to a general lack ofunderstanding of available procedures and models to adequately describe and quantify the risksassociated with key decision variables, no attempt was previously made in South Africa toincorporate production risk into DLP models with the aim of optimising agricultural water use.Problem statement and formulation of objectivesThe necessity to apply stochastic dynamic planning methods to evaluate alternative waterallocation policies and water use strategies has become more acute in the presence ofincreased risk faced by farmers as well as increasing scarcity of water supplies. The lack of ageneralised model that is able to optimise agricultural water use between multiple crops whiletaking into account the dynamic and stochastic environment within which decisions are made,hamper the development of appropriate optimal water use plans in the agricultural sector.The main objective of this research was to develop a generalised whole-farm stochasticdynamic linear programming model to assist water user associations (WUA) with optimal wateruse within the framework of integrated catchment managementSpecific objectives were:1. To develop a generalised whole-farm dynamic linear programming (DLP) with the emphasison optimising water use between multiple crops taking the timing of water limitations on cropyield into account.2 To quantify long run production risk and to develop procedures to incorporate these risksinto the DLP model.3 To optimise water use for an irrigation scheme through the linking of the individualrepresentative DLP models.MethodologyThe research was done in Vaalharts, the largest irrigation scheme in South Africa whichcomprises a total of 37 000 hectares of land, farmed by some 680 landowners. Canals supplythe water to the irrigation plots. The two main canals, the northern canal and the western canal.feed a network of feeder and community canals. The water quota for the north and west canal is9 140 m 3 per ha, resulting in an annual water use right of 209 744 720 m 3 for the north canaland 57 143 280 m 3 for the west canal {Van Heerden, 2001). Flood irrigation is the dominantirrigation type followed by pivot irrigation. The most important cash crops in the valley are maize,groundnuts, wheat, lucerne and cotton and the most important long-term crop is pecan nuts.
Page 1 and 2: GENERALISED WHOLE-FARM STOCHASTICDY
Page 3 and 4: DISCLAIMERThis report has been revi
Page 5: A cknowledgements* Mr Abraham Bekke
Page 10: Executive Summaryseveral farmers us
Page 13 and 14: Table of contents2.6 CONCLUSIONS 22
Page 15 and 16: LIST OF TABLESTABLE 3.1: UTILISATIO
Page 17 and 18: Lys van figureFIGURE E1:ENVELOPE OF
Page 19 and 20: Introductioncrops compete for water
Page 21 and 22: IntroductionChapter 2 gives an over
Page 23 and 24: Literature reviewYieldY = /(ET)y- .
Page 25: Literature reviewThe Lagrangian mut
Page 32 and 33: Literature review 15water use betwe
Page 34 and 35: Literature review 17White, 1969). A
Page 36 and 37: Literature review 19the explicit ti
Page 38 and 39: Literature review 21The variance of
Page 40 and 41: CHAPTERSTUDYAREA3.1 GENERAL DESCRIP
Page 42 and 43: Study Area 25250Jan Feb Mar Apr May
Page 44 and 45: Study Area 27users. More specifical
Page 46 and 47: Study Area 29which the user may dis
Page 48 and 49: Descriplion of the multi-period ris
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Description of the multi-period ris
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Results and conclusions 735.2 RE-AL
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Results and conclusions 755.3 RE-AL
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Results and conclusions 11unrealist
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RESEARCH IMPLICATIONSGeneralWhile S
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Research implications 81• The imp
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REFERENCESANNANDALE, J.G., BENADE,
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References 85DE JUAN, J.A, TARJUELO
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References 87MARE, H.G. (1995). Mar
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References 89Orange-Riet water user
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Appendix A 91pivotlpivotlpivotlpivo
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Appendix A 93flood2flood2flood2floo
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Appendix A 95labour.labour,labour.l
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Appendix A 97floodlfloodlflood2floo
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Appendix A 99pivot.ml.cropB.y3pivot
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Appendix A 101flood2flood2flood2.pi
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Appendix A 103CanalNetwork. gins04/
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Appendix A 105yipivot1.pivot.ml.cro
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Appendix A 107farm2.floodlfarm2.flo
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Appendix B 109C(I,J) Cholesky decom
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Appendix B 111DISPLAY VC;* Whistle
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Appendix B 113CUD(rnd,I,t)LE ((DATA
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APPENDIXENTERPRISE BUDGETWHEAT CENT
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Appendix C 118MAIZE: CENTRE PIVOTIt
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Appendix C120GROUNDNUTS CENTRE PIVO
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Appendix C 122LUCERNE: FLOOD 2003It
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Appendix C 124COTTON: FLOOD 2003Ite
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Appendix D 1265cl_5c3_off5c45c6offo
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Appendix D 128sec_to_ter(con,conl)
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Appendix D 130TABLEpg_to_p(pg,p) li
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Appendix D132wt25wt2 6wt27wt28wt2 9
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Appendix D 134pivot 10/TABLEprices(
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APPENDIXPRODUCTION FUNCTION ENVELOP
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Appendix E 1388.5»• •* •»
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Appendix E 1403.63.2* * * * ••
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