DOI number: 10.5027/jnrd.v4i0.11
Photo credits: Professor Pua Bar (Kutiel)
[stag_toggle style=”stroke” title=”Authors” state=”open”]
Shira Dickler a* , Meidad Kissinger a
a Ben-Gurion University of the Negev, Israel
*Correponding author: email@example.com [stag_icon icon=”envelope-o” url=”” size=”15px” new_window=”no”][/stag_toggle]
[stag_toggle style=”stroke” title=”Abstract” state=”closed”]
The prevailing global livestock industry relies heavily on natural capital and is responsible for high emissions of greenhouse gases (GHG). In recent years, nations have begun to take more of an active role in measuring their resource inputs and GHG outputs for various products. However, up until now, most nations have been recording data for production, focusing on processes within their geographical boundaries. Some recent studies have suggested the need to also embrace a consumption-based approach. It follows that in an increasingly globalized interconnected world, to be able to generate a sustainable food policy, a full systems approach should be embraced. The case of Israeli meat consumption presents an interesting opportunity for analysis, as the country does not have sufficient resources or the climatic conditions needed to produce enough food to support its population. Therefore, Israel, like a growing number of other countries that are dependent on external resources, relies on imports to meet demand, displacing the environmental impact of meat consumption to other countries. This research utilizes a multi-regional consumption perspective, aiming to measure the carbon and land footprints demanded by Israeli cattle and chicken meat consumption, following both domestic production and imports of inputs and products. The results of this research show that the “virtual land” required for producing meat for consumption in Israel is equivalent to 62% of the geographical area of the country. Moreover, almost 80% of meat consumption is provided by locally produced chicken products but the ecological impact of this source is inconsequential compared to the beef supply chain; beef imports comprise only 13% of meat consumption in Israel but are responsible for 71% of the carbon footprint and 83% of the land footprint. The sources of Israel’s meat supply are currently excluded from environmental impact assessments of Israeli processes. However, they constitute a significant fraction of the system’s natural capital usage, so they must be included in a comprehensive assessment of Israel’s consumption habits. Only then can policy be created for a sustainable food system, and inter-regional sustainability be achieved.
[stag_toggle style=”stroke” title=”References” state=”closed”]
 Arce, A.; Marsden, T.K. The social construction of international food: a new research agenda. Economic Geography 1993, 293-311.
 Bouwman, A.; Van der Hoek, K.; Eickhout, B.; Soenario, I. Exploring changes in world ruminant production systems. Agricultural Systems 2005, 84, 121-153.
 Bruins, H. J. Risks to food security: Contingency planning for agri-mega-crises. In I. Helsloot, A. Boin, B. Jacobs & L. K. Comfort (Eds.), Mega-crises: Understanding the prospects, nature, characteristics, and the effects of cataclysmic events (pp. 342-355). Illinois, USA: CC Thomas. 2012.
 Bruinsma, J. World agriculture: towards 2015/2030; An FAO perspective; Earthscan: London, UK, 2003; pp 24-31.
 Carlsson-Kanyama, A. (1998). Climate change and dietary choices—how can emissions of greenhouse gases from food consumption be reduced? Food Policy, 23(3), 277-293.
 Carvalho, P. Country Pasture/Forage Resource Profiles—Brazil. http://www.fao.org/ag/AGP/AGPC/doc/Counprof/Brazil/Brazil.htm (accessed October 2, 2013).
 Cederberg, C.; Meyer, D.; Flysjö, A. Life cycle inventory of greenhouse gas emissions and use of land and energy in Brazilian beef production; SIK-Institutet för livsmedel och bioteknik: Sweden, 2009, pp 51-53.
 Central Bureau of Statistics (Israel). Import of Beef from China 2007-2011. cbs.gov.il (accessed October 29, 2013).
 Chapagain, A. K.; Hoekstra, A. Y. Water footprints of nations. UNESCO. 2004.
 Clapp, J. The distancing of waste: Overconsumption in a global economy. Confronting consumption 2002, 155-176.
 Cordell, D.; Drangert, J.; & White, S. The story of phosphorus: Global food security and food for thought. Glob. Environ. Chang. 2009, 19, 292-305.
 Cribb, J. The coming famine: the global food crisis and what we can do to avoid it; Univ of California Press: California, USA, 2010; pp 10-13.
 Daly, H. E. Beyond growth: the economics of sustainable development; Beacon Press: Boston, MA, USA, 1997; pp 61-72.
 Daly, H. E.; Czech, B.; Trauger, D. L.; Rees, W. E.; Grover, M.; Dobson, T.; Trombulak, S. C. Are we consuming too much—for what? Conserv. Biol. 2007, 21, 1359-1362.
 Dauvergne, P. Dying of consumption: Accidents or sacrifices of global morality? Global Environ. Polit. 2005, 5, 35-47.
 Dauvergne, P. The shadows of consumption: Consequences for the global environment; MIT Press: Cambridge, MA, USA, 2008; 3-18.
 DECC/DEFRA. 2011 Guidelines to Defra/DECC’s GHG conversion factors for company reporting. Department for Environment, Food and Rural Affairs and Department for Energy and Climate Change, London, 2011.
 Ehrlich, P. R.; Ehrlich, A. H. The population explosion; Simon and Schuster: New York, NY, USA, 1990.
 Eshel, G.; Martin, P. A. Diet, energy, and global warming. Earth Interact. 2006, 10, 1-17.
 FAOStat. faostat.fao.org (accessed September 24, 2013).
 Feed Mill (a). 2013.
 Feed Mill (b). 2013.
 Fiala, N. Meeting the demand: An estimation of potential future greenhouse gas emissions from meat production. Ecol. Econ. 2008, 67, 412-419.
 Frey, S.; Barrett, J. In In Our health, our environment: the ecological footprint of what we eat; International Ecological Footprint Conference, Cardiff; 2007; pp 8-10.
 Galli, A.; Wiedmann, T.; Ercin, E.; Knoblauch, D.; Ewing, B.; Giljum, S. Integrating ecological, carbon and water footprint into a “footprint family” of indicators: Definition and role in tracking human pressure on the planet. Ecol. Ind. 2012, 16, 100-112.
 Gavrieli, R. Personal Interview. 2013.
 Gerber, P.J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., Falcucci, A. & Tempio, G. Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome. 2013.
 Grey, M. A. The industrial food stream and its alternatives in the United States: An introduction. Hum. Organ. 2000, 59, 143-150.
 Gupta, J. Global sustainable food governance and hunger: Traps and tragedies. Br. Food J. 2004, 106, 406-416.
 Haklay, N. Personal Interview. 2013.
 Israel Energy Company (IEC). Carbon footprint calculator. Retrieved June 10, 2013, from http://www.iec.co.il/environment/Pages/PollCalculator.aspx.
 Israel Slaughterhouse (a). 2013.
 Israel Slaughterhouse (b). 2013.
 Israeli Poultry Division. Quantitative data for chicken meat 2009-2013 [statistics]. Retrieved June 10, 2013 from http://www.ofotm.org.il.
 Jackson, T. Negotiating sustainable consumption: a review of the consumption debate and its policy implications. Energy Environ. 2004, 15, 1027-1051.
 Kastner, T.; Kastner, M.; Nonhebel, S. Tracing distant environmental impacts of agricultural products from a consumer perspective. Ecol. Econ. 2011, 70, 1032-1040.
 Kissinger, M. Approaches for calculating a nation’s food ecological footprint—The case of Canada. Ecol. Ind. 2013, 24, 366-374.
 Kissinger, M.; Gottlieb D. Place oriented ecological footprint analysis—The case of Israel’s grain supply. Ecol. Econ.2010, 69, 1639-1645.
 Kissinger, M.; Rees, W. E. Footprints on the prairies: Degradation and sustainability of Canadian agricultural land in a globalizing world. Ecol. Econ. 2009, 68, 2309-2315.
 Kissinger, M.; Rees, W. E.; Timmer, V. Interregional sustainability: governance and policy in an ecologically interdependent world. Environ. Sci. & Policy 2011, 14, 965-976.
 Lang, T. The complexities of globalization: The UK as a case study of tensions within the food system and the challenge to food policy. Agriculture and Human Values 1999, 16, 169-185.
 Lang, T. Diet, health and globalization: five key questions. Proc. Nutr. Soc. 1999, 58, 335-343.
 Leip, A.; Weiss, F.; Wassenaar, T.; Perez, I.; Fellmann, T.; Loudjani, P.; Tubiello, F.; Grandgirard, D.; Monni, S.; Biala, K. Evaluation of the livestock sector’s contribution to the EU greenhouse gas emissions (GGELS)–final report. European Commission, Joint Research Centre 2010.
 Martinez, S. W. Vertical coordination in the pork and broiler industries: Implications for pork and chicken products (No. 34031). United States Department of Agriculture, Economic Research Service. 1999.
 Meier, T.; Christen, O.; Semler, E.; Jahreis, G.; Voget-Kleschin, L.; Schrode, A.; Artmann, M. Balancing virtual land imports by a shift in the diet. Using a land balance approach to assess the sustainability of food consumption. Germany as an example. Appetite 2014, 74, 20-34.
 Mogensen, L.; Hermansen, J. E.; Halberg, N.; Dalgaard, R.; Vis, J.; Smith, B. G. Life cycle assessment across the food supply chain. Sustainability in the food industry 2009, 35, 115.
 Moss, A. R.; Jouany, J.; Newbold, J. In In Methane production by ruminants: its contribution to global warming; Annales de Zootechnie; Paris: Institut national de la recherche agronomique, 1960-2000.: 2000; Vol. 49, pp 231-254.
 Munksgaard, J.; Pedersen, K. A. CO< sub> 2</sub> accounts for open economies: producer or consumer responsibility? Energy Policy 2001, 29, 327-334.
 OECD. OECD Review of Agricultural Policies: Israel. 2008.
 Pathak, H.; Jain, N.; Bhatia, A.; Patel, J.; Aggarwal, P. Carbon footprints of Indian food items. Agric., Ecosyst. Environ. 2010, 139, 66-73.
 Peters, G. P. From production-based to consumption-based national emission inventories. Ecol. Econ. 2008, 65, 13-23.
 Peters, G. M., Rowley, H. V., Wiedemann, S., Tucker, R., Short, M. D., & Schulz, M. Red meat production in Australia: Life cycle assessment and comparison with overseas studies. Environmental Science & Technology, 2010, 44, 1327-1332.
 Pimentel, D.; Pimentel, M., 2008.Food, energy, and society. CRC Press: Florida, USA, 2008.
 Pinstrup-Andersen, P.; Pandya-Lorch, R. Food Security and Sustainable Use of Natural Resources: A 2020 Vision. Ecological Economics, 1998, 26, 1-10.
 Port World Distance Calculation. http://www.portworld.com/map/ (accessed October 29, 2013).
 Princen, T. Consumption and environment: some conceptual issues. Ecol. Econ. 1999, 31, 347-363.
 Princen, T.; Maniates, M.; Conca, K, 2002.Confronting consumption;The MIT Press, Massachusetts, USA.
 Rees, W. E. Achieving sustainability: reform or transformation? Journal of planning literature. 1995, 9, 343-361.
 Rees, W. E. Ecological footprints and biocapacity: essential elements in sustainability assessment. Renewables-based technology: Sustainability assessment 2006, 143-157.
 Rosegrant, M. W.; Cline, S. A. Global food security: Challenges and policies. Science, 2003 302, 1917-1919.
 Sagee, G. Personal Interview. 2013.
 Scherr, S. J.; Yadav, S. Land degradation in the developing world: Implications for food, agriculture, and the environment to 2020. 1996.
 Schroeder, R.; Aguiar, L. K.; Baines, R. Carbon Footprint in Meat Production and Supply Chains. Journal of Food Science and Engineering 2012, 2, 652-665.
 Schwarzer, S. Growing Greenhouse Gas Emissions Due to Meat Production. UNEP Global Environmental Alert Service 2012.
 Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor, M.; Miller, H.L. (eds.), 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
 Steinfeld, H.; Gerber, P.; Wassenaar, T.; Castel, V.; Rosales, M.; De Haan, C. Livestock’s long shadow; FAO: Rome, Italy, 2006; pp 24-33.
 Subak, S. Global environmental costs of beef production. Ecological Economics 1999. 30, 79-91.
 Tamir, L. Personal Interview. 2013.
 Tzuk-Bar, U. Personal Communication. 2013.
 United Nations Educational Scientific and Cultural Organization (UNESCO) Water a shared responsibility. The United Nations World Water Development 2006, 2.
 Wallén, A.; Brandt, N.; Wennersten, R. Does the Swedish consumer’s choice of food influence greenhouse gas emissions? Environmental Science & Policy 2004. 7, 525-535.
 Weber, C. L.; Matthews, H. S. Food-miles and the relative climate impacts of food choices in the United States. Environ. Sci. Technol. 2008, 42, 3508-3513.
 Williams, A. G.; Audsley, E.; Sandard, D. L. Determining the environmental burdens and resource use in the production of agricultural and horticultural commodities. Defra Research Project ISO205; DEFRA and Cranfield University: Bedford, UK. 2006.
 Williams, A. G.; Pell, E.; Webb, J.; Tribe, E.; Evans, D.; Moorhouse, E.; Watkiss, P. Comparative Life Cycle Assessment of Food Commodities Procured for UK Consumption through a Diversity of Supply Chains. DEFRA Project FO0103. 2007.
Multilayer Artificial Neural Networks (ANNs) with the backpropagation algorithm were used to estimate the decrease in relative saturated conductivity due to an increase in sodicity and salinity. Data from the literature on the relative saturated hydraulic conductivity measured using water having levels of sodicity and salinity in different types of semiarid soils were used. The clay content of these soils is predominantly montmorillonite. The input data consisted of clay percentage, cation exchange capacity, electrolyte concentration, and estimated soil exchangeable sodium percentage at equilibrium stage with the solution applied. The data was divided into three groups randomly to meet the three phases required for developing the ANN model (i. e. training, evaluation, and testing).The activation function selected was the TANSIG layer in the middle, while the exit function was the PURELIN layer. The comparisons between the experimental and predicted data on relative saturated hydraulic conductivity during training and testing phases showed good agreement. This was evident from the statistical indicators used for the evaluation process. For the training phase, the values of mean absolute error (MAE), root mean square error (RMSE), the correlation coefficient (r) and the determination coefficient (R2) were 0.08, 0.13, 0.91, and 0.83, respectively. The performance of the ANN model was evaluated against a part of the data selected randomly form the whole set of data collected (i. e. data not used during the model testing phase). The resultant values for MAE and RMSE, r and R2 were 0.12, 0.16, 0.82 and 0.68, respectively. It should be noted that many factors were not considered, such as soil pH, type of clay, and organic matter, due to the limitations of the data available. Using these factors as input in ANNs might improve model predictions. However, the results suggested that the ANN model performs well in soils with very low levels of organic matter.
DOI number: 10.5027/jnrd.v4i0.05
This paper assessed encroachment of streams due to physical development in Wa Urban Area of the Upper West Region of Ghana. The assessment was informed by the recognition that the roles played by streams in flood control are undermined by physical developments. This affects sustainable urban development and renders the urban area vulnerable to floods. The assessment was based on the 300m buffer zone standard set by the Ghana Water Resources Commission as a protective zone for such streams in the country. It is mandatory to offset all physical development from this zone but that is not the situation on the ground. For the purpose of this study each buffer zone was divided into sub-buffer zones of 100m in order to appreciate how far development has moved into the prohibited buffer zone. The streams and physical structures were mapped with a Trimble GPS receiver while land owners and tenants were purposively selected and interviewed. The buffer zone and sub-buffer zones were defined using GIS and overlaid with map of the physical structures. The categories of structures found in the buffer zones were residential (93.4 %), commercial (5.1%), public (1.3%) and agriculture (0.2%). The results of the study indicates that more than 50 % of physical structures mapped are located in the inner buffer and the land acquisition process for development of these structures amongst others in Wa is mostly initiated by developers.
DOI number: 10.5027/jnrd.v4i0.02Read More
A vast array of scientific literature is concerned with simulation models. The aim of models is to predict the unknown situation as close to as real one. To do this, models are validated and examined for their performance under known condition. In this paper, commonly used model performance evaluation indices are overviewed and examined under different situations. Difference based, efficiency based (Nash and Sutcliffe coefficient, model efficiency of Loague and Green, Legates and McCabe’s index) and composite indices (such as index of agreement, d, and dr) were found ambiguous, inconsistent and not logical in many cases. A new index, Percent Mean Relative Absolute Error (PMRAE), is proposed which is found unambiguous, logical, straight-forward, and interpretable; thus can be used to evaluate model performance. The model evaluation performance ratings based on PMRAE are also suggested.
DOI number: 10.5027/jnrd.v4i0.01
There are very little attempts of DEM evaluation in such a disturbed or discontinuous surface (e.g., in tillage area). Present study aims to evaluate common interpolation methods (triangulation, nearest neighbor, natural neighbor, minimum curvature, multiquadratic radial basis function (MRBF), ordinary kriging, and inverse distance weight) in representing the detail topography of two different tillage types, namely bench terrace and furrow. Evaluation procedure was conducted through a stepwise analysis by using combination between the accuracy level (coefficient of determination (R2), mean error (ME) and standard deviation error (S)) and the shape similarity analysis. This study also shows the application of break-line function during the interpolation process in order to optimize some interpolation methods and the usage of drainage sink area as another step in evaluating DEM quality. To achieve the aim of this study, two field-size of dry-land agriculture (tegalan) were observed by using a set of total station Nikon DTM 322 with 3” angle accuracy. These plots, namely Tieng (1652 m²) and Buntu (673 m²), are situated in the upper part of Wonosobo District, Central Java Province, Indonesia. Tieng plot represents the bench terrace system embedded with stones on its terrace risers and showing relatively smooth ground surface. On the other side, Buntu plot shows the ridges and furrows system that lays perpendicularly to the contour lines. In terms of R², ME and S, there were slight differences in results between each method, except the multiquadratic radial basis function which was failed to generate terrace form in Tieng. The final result shows that triangulation is the best fit method followed by natural neighbour at representing the bench terraces in Tieng plot. In the case of furrow in Buntu plot, natural neighbour is the most accurate method. Despite its superiority at representing the bench terrace, triangulation has larger sink drainage area compared to natural neighbour. This study has confirmed the robustness of a stepwise analysis between quantitative and qualitative assessment techniques for DEM accuracy. A fine value of quantitative parameter does not necessarily mean that it will fairly possess a good spatial accuracy.
DOI number: 10.5027/jnrd.v3i0.13Read More
The upward movement of water by capillary rise from shallow water-table to the root zone is an important incoming flux. For determining exact amount of irrigation requirement, estimation of capillary flux or upward flux is essential. Simulation model can provide a reliable estimate of upward flux under variable soil and climatic conditions. In this study, the performance of model UPFLOW to estimate upward flux was evaluated. Evaluation of model performance was performed with both graphical display and statistical criteria. In distribution of simulated capillary rise values against observed field data, maximum data points lie around the 1:1 line, which means that the model output is reliable and reasonable. The coefficient of determination between observed and simulated values was 0.806 (r = 0.93), which indicates a good inter-relation between observed and simulated values. The relative error, model efficiency, and index of agreement were found as 27.91%, 85.93% and 0.96, respectively. Considering the graphical display of observed and simulated upward flux and statistical indicators, it can be concluded that the overall performance of the UPFLOW model in simulating actual upward flux from a crop field under variable water-table condition is satisfactory. Thus, the model can be used to estimate capillary rise from shallow water-table for proper estimation of irrigation requirement, which would save valuable water from over-irrigation.
DOI number: 10.5027/jnrd.v3i0.12
Many regions of the world face the challenge to ensure high yield with limited water supply. This calls for utilization of available water in an efficient and sustainable manner. Quantitative models can assist in management decision and planning purposes. The FAO’s newly developed crop-water model, AquaCrop, which simulates yield in response to water, has been calibrated for winter wheat and subsequently used to simulate yield under different sowing dates, irrigation frequencies, and irrigation sequences using 10 years daily weather data. The simulation results suggest that “2 irrigation frequency” is the most water-efficient schedule for wheat under the prevailing climatic and soil conditions. The results also indicate decreasing yield trend under late sowing. The normal/recommended sequence of irrigation performed better than the seven-days shifting from the normal. The results will help to formulate irrigation management plan based on the resource availability (water, and land availability from previous crop).
DOI number: 10.5027/jnrd.v3i0.09
This research was conducted at the southern coastal area of central Java Island, Indonesia. It is aimed to identify several coastal vegetation characteristics for development of guideline for planning and design of tsunami mitigation. Survey method was applied to observe common coastal vegetation in the research area. Data collected from the survey consisted of vegetation parameters and coastal morphology. All selected vegetations were analyzed for their allometry relation of each species, maximum density, correlation between breaking moment and trunk diameter of each tree species, and correlation between trunk diameter and spacing between trees for each species. For coastal morphology, it was focused on topography and elevation from sea level.
The results show that trees with the hard wood will be stronger to hold the pull moment on the main trunk. Younger trees with smaller diameter tend to be more flexible, thus they will unbreakable during the test. The other trees which have flexible trunk such as Terminalia catappa and Anacardium occidentale were often pulled out their roots than broken on their trunks. To obtain more extensive characteristic, it is necessary to carry out advanced measurements, especially on the older trees which have more than 10 cm diameter.
Coastal areas consist of mud and sand materials tend to have a high tsunami risk, although mitigation treatments were different for both types. At the muddy area, the recommended vegetation are Avicennia marina and Rhizophora mucronata, meanwhile Casuarina equisetifolia and Anacardium occidentale, due to their high flexibility, will be more suitable on the sandy coast. Both types should be planted parallel to the shoreline. Casuarina is planted in the frontline followed by Anacardium behind it.
DOI number: 10.5027/jnrd.v3i0.07
The organophosphorus (OP) pesticide (malathion) is used heavily for many crops such as vegetable and cotton to control serious key insecticide in many areas of Egypt. This study has focused on the effect of malathion on aquatic environment and aquatic organisms. The experimental work was carried out using malathion at different dosage of water lasted 96h and was carried out undertaken laboratory conditions. It evaluated the sensitivity of organic toxic waste and their purification techniques for common carp, Cyprinus carpio by determining enzymes activity as biomarker indicators in various organs of the studied fish.
The results showed that exposure to malathion caused a significant increase in enzyme activity and total protein contents in the investigated tissues and inhibition of brain and liver acetylcholinesterase (AChE). Moreover, among the tissues studied, it appeared that the brain, gills and liver were more sensitive to pollution and seemed to be the most appropriate tissues to monitor water pollution by OP pesticides. In this context for environmental biomonitoring, the evaluation of toxic waste purification can be done to assess sensitivity of aquatic organism in recycling water to meet national goals and environmental safety.
DOI number: 10.5027/jnrd.v3i0.03