Abstract: Exploitation of alternative water sources is expected to grow in the decades to come in water-stressed countries with fast population growth, especially in regions where a further decline of natural freshwater availability is expected due to climate change. Increasing utilization of non-freshwater usually leads to salinity build-up in fields and water sources as well as accumulation of various pollutants — both having a considerable impact on the suitability of non-freshwater for irrigation due to constraints associated with crop salinity tolerance and food safety regulations. We developed a linked Computable General Equilibrium (CGE) — farm-level model of a water economy with representation for multiple water types characterized by different qualities. We employ the model to assess the impact of water shortage on the Israeli economy, where steadily growing water scarcity leads to an increasing utilization of alternative water sources. We simulate water shortage scenarios based on the Long Term National Master Plan for The Water Economy developed by the Israeli Water Authority (IWA). The linked CGE — farm-level model provides a mechanism for estimating the Constant Elasticity of Substitution (CES) rates between different irrigation water types used in agriculture. This mechanism accounts for the effects of salinity on yields and takes into consideration food safety regulations for irrigating crops with treated wastewater. We demonstrate that, in contrast to previous studies, CES rates between different water types are not identical. The CES rates obtained in our study have relatively low values, which can be attributed to the constraints associated with crop salinity tolerance and food safety regulations. Our results reveal that water shortage can lead to a significant decline of Israel’s GDP, where a considerable part of the decline is attributed to the decrease in agricultural outputs. The magnitude of the impact depends on the underlying assumptions regarding future desalination capacity. To further study the effect of desalination, we run simulations under various desalination levels and examine its impact on the GDP. We also examine the extent to which the impact of water shortage is sensitive to CES rates between different irrigation water types.

Abstract: Livestock directly contribute to greenhouse gas (GHG) emissions mainly through methane (CH4) and nitrous oxide (N2O) emissions. For cost and practicality reasons, quantification of GHG has been through development of various types of mathematical models. This chapter addresses the utility and limitations of mathematical models used to estimate enteric CH4 emissions from livestock production. Models used in GHG quantification can be broadly classified into either empirical or mechanistic models. Empirical models might be easier to use because they require fewer input variables compared with mechanistic models. However, their applicability in assessing mitigation options such as dietary manipulation may be limited. The major driving variables identified for both types of models include feed intake, lipid and nonstructural carbohydrate content of the feed, and animal variables. Knowledge gaps identified in empirical modeling were that some of the assumptions might not be valid because of geographical location, health status of animals, genetic differences, or production type. In mechanistic modeling, errors related to estimating feed intake, stoichiometry of volatile fatty acid (VFA) production, and acidity of rumen contents are limitations that need further investigation. Model prediction uncertainty was also investigated, and, depending on the intensity and source of the prediction uncertainty, the mathematical model may inaccurately predict the observed values with more or less variability. In conclusion, although there are quantification tools available, global collaboration is required to come to a consensus on quantification protocols. This can be achieved through developing various types of models specific to region, animal, and production type using large global datasets developed through international collaboration.