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Journal of Arid Land  2017, Vol. 9 Issue (4): 489-503    DOI: 10.1007/s40333-017-0058-7     CSTR: 32276.14.s40333-017-0058-7
Orginal Article     
Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province, Central Iran
JABERALANSAR Zahra*(), TARKESH Mostafa, BASSIRI Mehdi, POURMANAFI Saeid
Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran
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Abstract  

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network (GRNN) model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998-2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data (including climate and topography data), and the performance of each network model was assessed using the mean estimation error (MEE), model efficiency factor (MEF), and correlation coefficient (r). The best neural network model was then selected and further applied to predict the forage production of rangelands in the future (in 2030 and 2080) under A1B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production (0-100 kg/hm2) will increase while the areas with moderate, moderately high and high levels of forage production (≥100 kg/hm2) will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.



Key wordsrangelands      forage production      climate change scenario      generalized regression neural network      Central Iran     
Received: 27 July 2016      Published: 10 August 2017
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Cite this article:

JABERALANSAR Zahra, TARKESH Mostafa, BASSIRI Mehdi, POURMANAFI Saeid. Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province, Central Iran. Journal of Arid Land, 2017, 9(4): 489-503.

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http://jal.xjegi.com/10.1007/s40333-017-0058-7     OR     http://jal.xjegi.com/Y2017/V9/I4/489

1 Abdollahi J, Arzani H, Naderi H, et al.2012. Effect of precipitation and high temperature variability on forage production of some plant species in the Yazd steppe rangelands during the period of 2000-2008 (Case study: Ernan region). Arid Biome Scientific and Research Journal, 2(1): 58-69. (in Persian)
2 Ahmed J, Bonham C D, Laycock W A.1983. Comparison of techniques used for adjusting biomass estimates by double sampling. Journal of Range Management, 36(2): 217-221.
3 Ardestani E G, Tarkesh M, Bassiri M, et al.2015. Potential habitat modeling for reintroduction of three native plant species in central Iran. Journal of Arid Land, 7(3): 381-390.
4 Beers T W, Dress P E, Wensel L C.1966. Aspect transformation in site productivity research. Journal of Forestry, 64: 691-692.
5 Brown J, MacLeod N.2011. A site-based approach to delivering rangeland ecosystem services. The Rangeland Journal, 33(2): 99-108.
6 Cable D R.1975. Influence of precipitation on perennial grass production in the semidesert southwest. Ecology, 56(4): 981-986.
7 Celikoglu H B.2006. Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling. Mathematical and Computer Modelling, 44(7-8): 640-658.
8 Christensen L, Coughenour M B, Ellis J E, et al.2004. Vulnerability of the Asian typical steppe to grazing and climate change. Climatic Change, 63(3): 351-368.
9 Cigizoglu H K, Alp M.2006. Generalized regression neural network in modelling river sediment yield. Advances in Engineering Software, 37(2): 63-68.
10 Cohn D A, Ghahramani Z, Jordan M I.1996. Active learning with statistical models. Journal of Artificial Intelligence Research, 4: 129-145.
11 Cutler M E J, Boydb D S, Foodyb G M, et al.2012. Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions. ISPRS Journal of Photogrammetry and Remote Sensing, 70: 66-77.
12 Daly C, Gibson W P, Taylor G H, et al.2002. A knowledge-based approach to the statistical mapping of climate. Climate Research, 22(2): 99-113. ?
13 Ehsani A, Farahpour M, Jalili A, et al.2007. The effect of climatic conditions on range forage production in steppe ranglands, Akhtarabad of Saveh. Iranian Journal of Range and Desert Research, 14(2): 249-260.
14 Elzinga C L, Salzer D W, Willoughby J W.1998?. Measuring & Monitoring Plant Populations. Denver, Colorado: U.S. Department of the Interior, Bureau of Land Management, National Applied Resource Sciences Center, 471. (in Persian)
15 Engel E C, Weltzin J F, Norby R J, et al.2009. Responses of an old-field plant community to interacting factors of elevated [CO2], warming, and soil moisture. Journal of Plant Ecology, 2(1): 1-11.
16 Epstein H E, Lauenroth W K, Burke I C.1997. Effects of temperature and soil texture on ANPP in the U.S. Great Plains. Ecology, 78(8): 2628-2631.
17 FAO.1992. Report on the round table on pastoralism. FAO Technical Cooperation Programme, Project TCP/IRA/2255. Rome: FAO.
18 Field C B, Behrenfeld M J, Randerson J T, et al.1998. Primary production of the biosphere: integrating terrestrial and oceanic components. Science, 281(5374): 237-240.
19 Francis R C, Van Dyne G M, Williams B K.1979. An evaluation of weight estimation double sampling as a method of botanical analysis. Journal of Environmental Management, 8: 55-72.
20 Gang C, Zhou W, Wang Z, et al.2015. Comparative assessment of grassland NPP dynamics in response to climate change in China, North America, Europe and Australia from 1981 to 2010. Journal of Agronomy and Crop Science, 201(1): 57-68.
21 Gao Y H, Zhou X, Wang Q, et al.2013. Vegetation net primary productivity and its response to climate change during 2001-2008 in the Tibetan Plateau. Science of the Total Environment, 444: 356-362.
22 George M R, Williams W A, McDougald N K, et al.1989. Predicting peak standing crop on annual range using weather variables. Journal of Range Management, 42(6): 508-513.
23 González-Megías A, Menéndez R.2012. Climate change effects on above- and below-ground interactions in a dryland ecosystem. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1606): 3115-3124.
24 Goraghani H R S, Sardo M S, Azizi N, et al.2014. Investigation of changes in rangeland vegetation regarding different slopes, elevation and geographical aspects (Case Study: Yazi Rangeland, Noor County, Iran). Journal of Rangeland Science, 4(3): 246-255.
25 Guisan A, Zimmermann N E.2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135(2-3): 147-186.
26 Hanafin J A, McGrath R, Semmler T, et al.2011. Air flow and stability indices in GCM future and control runs. International Journal of Climatology, 31(8): 1240-1247.
27 Hannan S A, Manza R R, Ramteke R J.2010. Generalized regression neural network and radial basis function for heart disease diagnosis. International Journal of Computer Applications, 7(13): 7-13.
28 Havstad K M, Peters D P C, Skaggs R, et al.2007. Ecological services to and from rangelands of the United States. Ecological Economics, 64(2): 261-268.
29 Holechek J L.1988. An approach for setting the stocking rate. Rangelands, 10(1): 10-14.
30 Humphrey R R.1949. Field comments on the range condition method of forage survey. Journal of Range Management, 2(1): 1-10.
31 Ingram J C, Dawson T P, Whittaker R J.2005. Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sensing of Environment, 94(4): 491-507.
32 IPCC. 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: Cambridge University Press, 989.
33 IPCC. 2013. Annex III: Glossary. In: Stocker T F, Qin D, Plattner G K, et al. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom, New York, NY, USA: Cambridge University Press, 1447-1466.
34 Kawada K, Suzuki K, Suganuma H, et al.2012. Plant biodiversity in the semi-arid zone of Tunisia. Journal of Arid Land Studies, 22(1): 83-86.
35 Khanum R, Mumtaz A S, Kumar S.2013. Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling. Acta Oecologica, 49: 23-31.
36 Knapp A K, Smith M D.2001. Variation among biomes in temporal dynamics of aboveground primary production. Science, 291(5503): 481-484.
37 K?chy M, Mathaj M, Jeltsch F, et al.2008. Resilience of stocking capacity to changing climate in arid to Mediterranean landscapes. Regional Environmental Change, 8(2): 73-87.
38 Krinner G, Viovy N, de Noblet-Ducoudré N, et al.2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles, 19(1): GB1015.
39 Lauenroth W K, Sala O E.1992. Long-term forage production of North American shortgrass steppe. Ecological Applications, 2(4): 397-403.
40 Lee S, Park I, Koo B J, et al.2013. Macrobenthos habitat potential mapping using GIS-based artificial neural network models. Marine Pollution Bulletin, 67(1-2): 177-186.
41 Lek S, Guégan J F.1999. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling, 120(2-3): 65-73.
42 Liang E Y, Shao X M, Kong Z C, et al.2003. The extreme drought in the 1920s and its effect on tree growth deduced from tree ring analysis: A case study in north China. Annals of Forest Science, 60(2): 145-152.
43 Linderman M, Liu J, Qi J, et al.2004. Using artificial neural networks to map the spatial distribution of understorey bamboo from remote sensing data. International Journal of Remote Sensing, 25(9): 1685-1700.
44 Luo T X, Li W H, Zhu H Z.2002. Estimated biomass and productivity of natural vegetation on the Tibetan Plateau. Ecological Applications, 12(4): 980-997.
45 Melillo J M, Mcguire A D, Kicklighter D W, et al.1993. Global climate change and terrestrial net primary production. Nature, 363(6426): 234-240.
46 Miehe S, Kluge J, Von Wehrden H, et al.2010. Long-term degradation of Sahelian rangeland detected by 27 years of field study in Senegal. Journal of Applied Ecology, 47(3): 692-700.
47 Mowll W, Blumenthal D M, Cherwin K, et al.2015. Climatic controls of aboveground net primary production in semi-arid grasslands along a latitudinal gradient portend low sensitivity to warming. Oecologia, 177(4): 959-969.
48 Munasinghe M.2009.Sustainable Development in Practice: Sustainomics Methodology and Applications. Cambridge: Cambridge University Press, 652.
49 Nemani R R, Keeling C D, Hashimoto H, et al.2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625): 1560-1563.
50 O’Mara F P.2012. The role of grasslands in food security and climate change. Annals of Botany, 110(6): 1263-1270.
51 Olden J D, Lawler J J, Poff N L.2008. Machine learning methods without tears: a primer for ecologists. The Quarterly Review of Biology, 83(2): 171-193.
52 Prudhomme C, Wilby R L, Crooks S, et al.2010. Scenario-neutral approach to climate change impact studies: application to flood risk. Journal of Hydrology, 390(3-4): 198-209.
53 Rathore A, Jasrai Y T.2013. Growth and chlorophyll levels of selected plants with varying photosynthetic pathways (C3, C4 and CAM). International Journal of Scientific & Engineering Research, 4(2): 1-4.
54 Ray R, Gururaja K V, Ramchandra T V.2011. Predictive distribution modeling for rare Himalayan medicinal plant Berberis aristata DC. Journal of Environmental Biology, 32(6): 725-730.
55 Reynolds J F, Smith D M S, Lambin E F, et al.2007. Global desertification: Building a science for dry land development. Science, 316(5826): 847-851.
56 Rummukainen M.2012. Changes in climate and weather extremes in the 21st century. Climate Change, 3(2): 115-129.
57 Schuur E A, Matson P A.2001. Net primary productivity and nutrient cycling across a mesic to wet precipitation gradient in Hawaiian montane forest. Oecologia, 128(3): 431-442.
58 Scurlock J M O, Johnson K, Olson R J.2002. Estimating net primary productivity from grassland biomass dynamics measurements. Global Change Biology, 8(8): 736-753.
59 Shaw M R, Pendleton L, Cameron D R, et al.2011. The impact of climate change on California’s ecosystem services. Climatic Change, 109(Suppl.): 465-484.
60 Sitch S, Smith B, Prentice I C, et al.2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9(2): 161-185.
61 Smith M D, La Pierre K J, Collins S L, et al.2015. Global environmental change and the nature of aboveground net primary productivity responses: insights from long-term experiments. Oecologia, 177(4): 935-947.
62 Smoliak S.1956. Influence of climatic conditions on forage production of shortgrass rangeland. Journal of Range Management, 9(2): 89-91.
63 Steffen W L, Walker B H, Ingram J S, et al.1992. Global change and terrestrial ecosystems: The operational plan. Global Change Report 21. Stockholm, Sweden: IGBP, 95.
64 Tanaka K, Taino S, Haraguchi H, et al.2012. Warming off southwestern Japan linked to distributional shifts of subtidal canopy-forming seaweeds. Ecology and Evolution, 2(11): 2854-2865.
65 Torell L A, Lyon K S, Godfrey E B.1990. Long-run versus short-run planning horizons and the rangeland stocking rate decision. American Journal of Agricultural Economics, 73(3): 795-807.
66 Wilds S, Boetsch J R, van Manen F T, et al.2000. Modeling the distributions of species and communities in Great Smoky Mountains National Park. Computers and Electronics in Agriculture, 27(1-3): 389-392.
67 Wilm H G, Costello D F, Klipple G E.1944. Estimating forage yield by the double-sampling method. Journal of the American Society of Agronomy, 36(3): 194-203.
68 Wu X Y, Zhang X F, Dong S K, et al.2015. Local perceptions of rangeland degradation and climate change in the pastoral society of Qinghai-Tibetan Plateau. The Rangeland Journal, 37(1): 11-19.
69 Zar J H.2010. Biostatistical Analysis (5th ed.). New Jersey: Pearson Prentice Hall, 944.
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