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The Sulphurdale Geothermal Field is located in Beaver County Utah, within the boundaries of the Cove Fort--Sulphurdale Known Geothermal Resource Area (KGRA). During the past year, three wells drilled in Section 7, T-26-S, R-6-W, have produced dry steam from a fractured volcanic formation located at a depth of about 1100 feet. Two of these three wells are currently prepared to supply steam to a power plant, and one well has been plugged and abandoned. ThermaSource, Inc. was retained by Mother Earth Industries, the operator of the field, to conduct well tests and render an opinion as to the nature of the geothermal reserves and assess the commercial potential of these reserves. Because of the limited area that has been explored to date, there can be no assurance that the reserves estimate will prove accurate. Project economics are based on parameters believed to be accurate, but there is no assurance that such cash flow projections will be realized.

The Sulphurdale Geothermal Field is located in Beaver County Utah, within the boundaries of the Cove Fort--Sulphurdale Known Geothermal Resource Area (KGRA). During the past year, three wells drilled in Section 7, T-26-S, R-6-W, have produced dry steam from a fractured volcanic formation located at a depth of about 1100 feet. Two of these three wells are currently prepared to supply steam to a power plant, and one well has been plugged and abandoned. ThermaSource, Inc. was retained by Mother Earth Industries, the operator of the field, to conduct well tests and render an opinion as to the nature of the geothermal reserves and assess the commercial potential of these reserves. Because of the limited area that has been explored to date, there can be no assurance that the reserves estimate will prove accurate. Project economics are based on parameters believed to be accurate, but there is no assurance that such cash flow projections will be realized.

Scarcity of water resources in the entire country is more serious in the northern provinces like Golestan. Therefore, farmers have to use saline water or waste waters. One way to adapt to this condition is to use unconventional water such as Caspian Sea water, with lower salinity as compared with water from free seas, for common crops like soybean. Thus feasibility of using Caspian Sea water that has integrated with narrow common water and the calibration of Aquacrop model under the effect of salinity and water stress for soybean crop in Golestan province are more important. The Aquacrop model assumes a linear relationship between the biomass yield (BY) and crop transpiration one one hand and water productivity (WP) value on the other (Steduto and Albrizio, 2005). This model, as compared to other crop models, requires minimal input data and its new version 4.0 (June, 2012) has a salinity module which was used in this research to simulate the grain yield (GY) and WP of soybean under deficit and saline water irrigation. Furthermore, the AquaCrop model has not often been tested to simulate the yield of soybean under saline conditions in the semi-arid climate of Golestan province. This study aims at evaluating the efficiency of AquaCrop model in soybean yield simulation. The results are to be used for optimizing water consumption under water and saline stress. To this end, an experiment was carried out at the research farm of Gorgan University of Agricultural Sciences and Natural Resources.

Scarcity of water resources in the entire country is more serious in the northern provinces like Golestan. Therefore, farmers have to use saline water or waste waters. One way to adapt to this condition is to use unconventional water such as Caspian Sea water, with lower salinity as compared with water from free seas, for common crops like soybean. Thus feasibility of using Caspian Sea water that has integrated with narrow common water and the calibration of Aquacrop model under the effect of salinity and water stress for soybean crop in Golestan province are more important. The Aquacrop model assumes a linear relationship between the biomass yield (BY) and crop transpiration one one hand and water productivity (WP) value on the other (Steduto and Albrizio, 2005). This model, as compared to other crop models, requires minimal input data and its new version 4.0 (June, 2012) has a salinity module which was used in this research to simulate the grain yield (GY) and WP of soybean under deficit and saline water irrigation. Furthermore, the AquaCrop model has not often been tested to simulate the yield of soybean under saline conditions in the semi-arid climate of Golestan province. This study aims at evaluating the efficiency of AquaCrop model in soybean yield simulation. The results are to be used for optimizing water consumption under water and saline stress. To this end, an experiment was carried out at the research farm of Gorgan University of Agricultural Sciences and Natural Resources.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

Points out that sustainability as such does not provide a clearcut guide to policy. First one has to decide what is to be sustained. If this is agreed, it must be in an operational from. However, difficulties may still emerge since opinions may differ about how to achieve. This is illustrated by differences in the views of economists about how sustainable development is to be achieved. Orthodox economists stress the importance of the accumulation of man-made capital to achieve this end whereas neo-Malthusians stress the importance of conserving natural resource and environmental capital. Both take an anthropocentric point of view. For political reasons the neo-Malthusian has had little support but it may eventually turn out to be correct. Economics is concerned with reducing economic scarcity and economists have traditionally suggested four main ways of doing this of which economic growth is one. However, neo-Malthusian economists believe that this may not be a sustainable strategy – it may result in future poverty. It should be noted that economic systems are embedded in social and natural systems and depend on these. Economic sustainability depends on the sustainability of these other systems. So from this point of view, it is just one of several bottom lines. Values must be considered in relation to sustainability. Economics is completely anthropocentric in its approach. Therefore, economic approaches to conservation and sustainability can be at odds with the values of deep ecologists or those willing to accord rights to other sentient beings or ecosystems independent of human wishes, or those who want to make use of value judgments other than those based on the measuring rod of money. Consequently economics evaluation is sometimes ineffective in resolving social conflict, including conflict about what should be sustained. As a rule economics alone should not be the final arbiter of social decisions. It is a part (often an important part) of the social evaluation process but not the bottom line, or just one of many lines.

Points out that sustainability as such does not provide a clearcut guide to policy. First one has to decide what is to be sustained. If this is agreed, it must be in an operational from. However, difficulties may still emerge since opinions may differ about how to achieve. This is illustrated by differences in the views of economists about how sustainable development is to be achieved. Orthodox economists stress the importance of the accumulation of man-made capital to achieve this end whereas neo-Malthusians stress the importance of conserving natural resource and environmental capital. Both take an anthropocentric point of view. For political reasons the neo-Malthusian has had little support but it may eventually turn out to be correct. Economics is concerned with reducing economic scarcity and economists have traditionally suggested four main ways of doing this of which economic growth is one. However, neo-Malthusian economists believe that this may not be a sustainable strategy – it may result in future poverty. It should be noted that economic systems are embedded in social and natural systems and depend on these. Economic sustainability depends on the sustainability of these other systems. So from this point of view, it is just one of several bottom lines. Values must be considered in relation to sustainability. Economics is completely anthropocentric in its approach. Therefore, economic approaches to conservation and sustainability can be at odds with the values of deep ecologists or those willing to accord rights to other sentient beings or ecosystems independent of human wishes, or those who want to make use of value judgments other than those based on the measuring rod of money. Consequently economics evaluation is sometimes ineffective in resolving social conflict, including conflict about what should be sustained. As a rule economics alone should not be the final arbiter of social decisions. It is a part (often an important part) of the social evaluation process but not the bottom line, or just one of many lines.

In recent years, food security, especially supply, has been an important issue in China’s agricultural production. The stability of grain prices is related to the stability and development of the grain market. Based on agricultural production data from 1970 to 2015, this paper explores the influence of agricultural product price fluctuation on grain production by using the cobweb theory and vector error correction (VEC) model. The results show that changes in grain production in China are affected by fluctuations in agricultural product prices, that the production change lags behind the price change, and that there is a long-term equilibrium relationship between grain yield and agricultural product price. A Granger causality test shows that the change in agricultural product price is the Granger cause of grain yield change.