Irrigated agriculture and climate change: The influence of water supply variability and salinity on adaptation

Connor, Jeffery D.; Schwabe, K.; King, Darran; Knapp, Keith C.

doi:10.1016/j.ecolecon.2012.02.021

Cited by 102 publications

(53 citation statements)

References 14 publications

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“…Connor et al [38] noted that an increasing number of analyses assess the impacts of climate change on irrigated agriculture in arid and semi-arid regions of the world, especially those that face a projection of drier weather. The objective function of their irrigation sector model maximizes profits across three sub-regions in the Murray-Darling River basin, Australia, subject to land and water constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing Climate Change Impacts on Water Resources and Colorado Agriculture Using an Equilibrium Displacement Mathematical Programming Model

Fathelrahman

Davies

et al. 2014

Water

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This research models selected impacts of climate change on Colorado agriculture several decades in the future, using an Economic Displacement Mathematical Programming model. The agricultural economy in Colorado is dominated by livestock, which accounts for 67% of total receipts. Crops, including feed grains and forages, account for the remainder. Most agriculture is based on irrigated production, which depends on both groundwater, especially from the Ogallala aquifer, and surface water that comes from runoff derived from snowpack in the Rocky Mountains. The analysis is composed of a Base simulation, designed to represent selected features of the agricultural economy several decades in the future, and then three alternative climatic scenarios are run. The Base starts with a reduction in agricultural water by 10.3% from increased municipal and industrial water demand, and assumes a 75% increase in corn extracted-ethanol production. From this, the first simulation (S1) reduces agricultural water availability by a further 14.0%, for a combined decrease of 24.3%, due to climatic factors and related groundwater depletion. The second simulation (S2-WET) describes wet year conditions, which negatively affect yields of irrigated corn and milking cows, but improves yields for OPEN ACCESSWater 2014, 6 1746 important crops such as non-irrigated wheat and forages. In contrast, the third simulation (S3-DRY) describes a drought year, which leads to reduced dairy output and reduced corn and wheat. Consumer and producer surplus losses are approximately $10 million in this simulation. The simulation results also demonstrate the importance of the modeling trade when studying climate change in a small open economy, and of linking crop and livestock activities to quantify overall sector effects. This model has not taken into account farmers' adaptation strategies, which would reduce the climate impact on yields, nor has it reflected climate-induced shifts in planting decisions and production practices that have environmental impacts or higher costs. It also focuses on a comparative statics approach to the analysis in order to identify several key effects of changes in water availability and yields, without having a large number of perhaps confounding assumptions.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing Climate Change Impacts on Water Resources and Colorado Agriculture Using an Equilibrium Displacement Mathematical Programming Model

Fathelrahman

Davies

et al. 2014

Water

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“…Water availability is expected to continue to be constrained due to population growth and economic development (Vörösmarty et al 2000), current unsustainable depletion of groundwater (Famiglietti 2014), and ongoing and future climate change (Elliott et al 2014;Diffenbaugh et al 2015). This reduction in agricultural water availability has led to increased fallowing of land (Connor et al 2012;Christian-Smith et al 2014) and the need of more efficient irrigation methods, including drip irrigation (Postel 2000;Gleick 2002;Ayars et al 2015). The reduction in agricultural water has also led to substantial increase in water prices; farmers in the highest priced regions (e.g., Southern Coastal California, USA; Israel) pay ~$1 or more per m 3 for the most expensive water (Howitt 2014;Ward and Becker 2015).…”

Section: Introductionmentioning

confidence: 99%

Incorporating field wind data to improve crop evapotranspiration parameterization in heterogeneous regions

et al. 2017

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to a potentially inaccurate ET 0 for irrigation scheduling. In this study, we investigated multiple ET 0 products from six meteorological stations, a satellite ET 0 product, and integration (merger) of two stations' data in Southern California, USA. We evaluated ET 0 against lysimetric ET observations from two lysimeter systems (weighing and volumetric) and two crops (wine grapes and Jerusalem artichoke) by calculating crop ET (ET c ) using crop coefficients for the lysimetric crops with the different ET 0 . ET c calculated with ET 0 products that incorporated field-specific wind speed had closer agreement with lysimetric ET, with RMSE reduced by 36 and 45% for grape and Jerusalem artichoke, respectively, with on-field anemometer data compared to wind data from the nearest station. The results indicate the potential importance of on-site meteorological sensors for ET 0 parameterization; particularly where microclimates are highly variable and/or irrigation water is expensive or scarce.

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“…Hence, irrigation water has to be treated properly to get rid of all the undesirable physical, chemical, and biological contaminants that are able to: (i) reduce the choice of crops (Levy and Tai, 2013); (ii) cut down crop yield (Rasouli et al, 2013); (iii) damage crop quality (Bernstein et al, 2011); (iv) injure soil appropriateness and (v) harm the irrigation tools (Connor et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Brackish groundwater membrane system design for sustainable irrigation: optimal configuration selection using analytic hierarchy process and multi-dimension scaling

Lew

Trachtengertz

Ratsin

et al. 2014

Front. Environ. Sci.

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The recent high demands for reuse of salty water for irrigation affected membrane producers to assess new potential technologies for undesirable physical, chemical, and biological contaminants removal. This paper studies the assembly options by the analytic hierarchy process (AHP) model and the multi-dimension scaling (MDS) techniques. A specialized form of MDS (CoPlot software) enables presentation of the AHP outcomes in a two dimensional space and the optimal model can be visualized clearly. Four types of 8" membranes were selected: (i) Nanofiltration low rejection and high flux (ESNA1-LF-LD, 86% rejection, 10,500 gpd); (ii) Nanofiltration medium rejection and medium flux (ESNA1-LF2-LD, 91% rejection, 8200 gpd); (iii) Reverse Osmosis high rejection and high flux (CPA5-MAX, 99.7 rejection, 12,000 gpd); and (iv) Reverse Osmosis medium rejection and extreme high flux (ESPA4-MAX, 99.2 rejection, 13,200 gpd). The results indicate that: (i) Nanofiltration membrane (High flux and Low rejection) can produce water for irrigation with valuable levels of nutrient ions and a reduction in the sodium absorption ratio (SAR), minimizing soil salinity; this is an attractive option for agricultural irrigation and is the optimal solution; and (ii) implementing the MDS approach with reference to the variables is consequently useful to characterize membrane system design.

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Irrigated agriculture and climate change: The influence of water supply variability and salinity on adaptation

Cited by 102 publications

References 14 publications

Assessing Climate Change Impacts on Water Resources and Colorado Agriculture Using an Equilibrium Displacement Mathematical Programming Model

Assessing Climate Change Impacts on Water Resources and Colorado Agriculture Using an Equilibrium Displacement Mathematical Programming Model

Incorporating field wind data to improve crop evapotranspiration parameterization in heterogeneous regions

Brackish groundwater membrane system design for sustainable irrigation: optimal configuration selection using analytic hierarchy process and multi-dimension scaling

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