Irrigation decision making model development

Temporal discretisation of soil moisture target

The specification of farmers’ intensive margin irrigation decision as the choice of a soil moisture target provides a more realistic representation of farmers’ intraseasonal irrigation scheduling, in comparison with existing hydro-economic analysis that assumes irrigation decisions are made in terms of the total seasonal depth of applied irrigation. However, when seasonal irrigation water supply is limited, the optimal soil moisture target may be non-constant during the growing season because the response of crop yield to water deficits varies according to phenological growth stage (Doorenbos and Kassam, 1979; Traore et al., 2000; Fereres and Soriano, 2007; Payero et al., 2009). To improve the developed modelling framework, it is recommended that the pre-season intensive margin irrigation decision is expanded to consider time-varying deficit irrigation scenarios. The temporal discretisation of the soil moisture target should be restricted to a small number of key growth stages that determine the majority of the temporal variability of crop yield response to water deficits, in order to maintain the computational tractability of the pre-season model. Furthermore, it is likely to be inefficient to consider all potential temporal combinations of soil moisture targets and, therefore, the range of deficit irrigation strategies that are considered for a given case study should be refined based on previous research (e.g., Heeren et al. (2011)) or using crop simulation models (e.g., Geerts et al. (2010)). As noted in Section 2.4.6, it is anticipated that inclusion of deficit irrigation strategies as a component of farmers’ irrigation

decision making may decrease the estimated sensitivity of irrigated agriculture to regulatory groundwater abstraction restrictions. Further analysis should seek to determine the magnitude of this effect, and the extent to which deficit irrigation may increase the long-term welfare and production gains from regulation of groundwater abstraction and other policies. However, it is important to note that deficit irrigation is unlikely to alter the negative impacts of low well yields on irrigated agriculture. Specifically, deficit irrigation will not be a viable adaptation strategy when well yields have already been reduced substantially due to the fact that irrigation capacity will be insufficient to avoid significant water stress effects during sensitive crop growth stages (Geerts and Raes, 2009).

Selection of crop mix

When water supply is limited, either by regulations or well yield, a farmer may seek to allocate land and other resources between crop types or varieties that have different total and/or peak water requirements. In this thesis, only one crop type (corn) has been analysed and it has been assumed that any field area that is not irrigated generates no economic value. Further improve- ments to the model of irrigation decision making are required to evaluate the effects of crop selection on the profitability of groundwater-fed irrigation under limited water supply. Multi- ple crop types could be considered by generating stochastic intraseasonal crop-water production functions for each potential crop choice. The use of the AquaCrop model may be advantageous here, as AquaCrop is capable of simulating a diverse range of crop types and requires calibration of only a relatively small number of model parameters compared with other more complex bio- physical crop simulation models (Steduto et al., 2009; Vanuytrecht et al., 2014). Subsequently, production functions for each crop type could be incorporated in to the economic model of pre- season irrigation decision making to determine optimal land allocation to different crop types for an expected level of irrigation water supply. It has been hypothesised in this thesis that di- versifying production, either fully or partially, to less water intensive or dryland agriculture may increase farmers’ ability to adapt to water supply restrictions (Section 2.4.6). Examples exist in the literature highlighting the potential importance of crop mix diversification as a response to seasonal water supply restrictions (e.g., García-Vila and Fereres (2012)) and increasing aquifer salinity (e.g., Grundmann et al. (2012)). However, the extent to which adjusting crop types can buffer farmers’ significantly against the long-term economic effects of declining well yields has yet to be explored adequately and further research would be valuable to quantify the potential importance of this behavioural response.

Impact of variable initial soil water content

The integrated model simulations conducted in Chapter 4 did not model changes in soil moisture outside of periods of crop growth or the effect of variable initial soil water content on the trajec- tory of irrigated production. As described in Section 4.2.3, continuous simulation of soil water content is feasible using the existing model framework, but would impose large computational demands due to the need to generate separate stochastic intraseasonal crop-water production

functions for each potential initial soil moisture level. Adaptation of the Matlab-AquaCrop

model code for parallel execution using the Matlab Parallel Computing Toolbox (Mathworks Inc., 2013), therefore, could be beneficial to enable the rapid pre-generation of multiple sets of stochastic intraseasonal crop-water production functions. Future analysis should seek to evaluate how changes in optimal irrigation decision making as a function of variable initial soil moisture levels (Section 2.4.3) influence the dynamic long-term trajectory of both irrigated production and the groundwater system. For example, a recent study by McGuire (2014) has shown that 13.5 % of total groundwater storage depletion since pre-development (circa 1950) in the Ogallala Aquifer in the United States occurred between 2011 and 2013 as a result of increased pumping during a sequence of drought events. Given the non-linear relationship between saturated thickness and well yield, this suggests that multi-year droughts could rapidly push groundwater systems beyond critical thresholds that negatively impact agricultural production. It would be interest- ing, therefore, to use the model developed in this thesis to explore the impacts that extended periods of drought, which depress soil moisture levels and may become more common as a result of climate change (Strzepek et al., 2010; Dai, 2011; Intergovernmental Panel on Climate Change, 2012), have on demand for groundwater, the rate of aquifer depletion, and the subsequent ability to manage groundwater resources sustainably.

Behavioural characteristics

The model developed in this thesis evaluates the land and groundwater use decision making of an individual utility-maximising farmer. The farmer is assumed to behave non-cooperatively and myopically, with irrigation decisions made annually and intraseasonally given expectations about weather, groundwater levels, well yields, and soil moisture. However, it is important to acknowledge that the decision making of real farmers may also have additional behavioural characteristics that have yet to be considered in the proposed modelling framework. Farmers, for example, may be motivated by non-economic objectives that differ from profit- or utility- maximisation (Janssen and van Ittersum, 2007), or may behave non-myopically by formulating long-term groundwater extraction plans (Madani and Dinar, 2012a; Suter et al., 2012; Guilfoos

et al., 2013). Decision making may also be influenced by how farmers characterise and update their expectations about uncertainty in factors influencing production, such as weather condi- tions, or crop and input prices (Ng et al., 2011; Finger, 2012). Furthermore, it may be relevant to consider how farmers are influenced by interactions with other farmers, and how the structure of agent interactions and coordination influence macro-level system responses (Berger et al., 2007; Ng et al., 2011; Madani and Dinar, 2012a). However, these examples are not an exhaustive list of all the potential factors that may influence farmer decision making. A key question there- fore is how to determine which additional behavioural characteristics are the most important to incorporate for a given application of the proposed modelling framework. In this context, future work might seek to use interviews, surveys and other empirical data collection techniques, such as laboratory experiments and role-playing games, to deduce the most significant factors influencing farmers’ individual decision making (Janssen and Ostrom, 2006; Suter et al., 2012). Alternatively, where sufficient data about field-level production decisions and changes in ground- water conditions exists, calibration techniques could be used to determine the model parameter sets that best describe historically observed production decisions (Bulatewicz et al., 2010; Holtz and Pahl-Wostl, 2012; Troost and Berger, 2014). However, given the complexities inherent in describing the heterogeneity of distributed farmer behaviour and feedbacks with the hydrolog- ical system, it is likely that no single model structure or parameter set will provide a perfect representation of the real-world coupled system. Future efforts therefore should seek to assess the effects of uncertainties in model representations of both individual farmer behaviour and the hydrological system, and, in turn, quantify formally how these uncertainties affect the robustness of predictions about future system trajectories and policy effectiveness.