Skip to main content
SpringerLink
Log in

Estimation of spatial and temporal changes in net primary production based on Carnegie Ames Stanford Approach (CASA) model in semi-arid rangelands of Semirom County, Iran

  • Published:
Journal of Arid Land Aims and scope Submit manuscript

Abstract

Net primary production (NPP) is an indicator of rangeland ecosystem function. This research assessed the potential of the Carnegie Ames Stanford Approach (CASA) model for estimating NPP and its spatial and temporal changes in semi-arid rangelands of Semirom County, Iran. Using CASA model, we estimated the NPP values based on monthly climate data and the normalized difference vegetation index (NDVI) obtained from the MODIS sensor. Regression analysis was then applied to compare the estimated production data with observed production data. The spatial and temporal changes in NPP and light utilization efficiency (LUE) were investigated in different rangeland vegetation types. The standardized precipitation index (SPI) was also calculated at different time scales and the correlation of SPI with NPP changes was determined. The results indicated that the estimated NPP values varied from 0.00 to 74.48 g C/(m2·a). The observed and estimated NPP values had different correlations, depending on rangeland conditions and vegetation types. The highest and lowest correlations were respectively observed in Astragalus spp.-Agropyron spp. rangeland (R2=0.75) with good condition and Gundelia spp.-Cousinia spp. rangeland (R2=0.36) with poor and very poor conditions. The maximum and minimum LUE values were found in Astragalus spp.-Agropyron spp. rangeland (0.117 g C/MJ) with good condition and annual grasses-annual forbs rangeland (0.010 g C/MJ), respectively. According to the correlations between SPI and NPP changes, the effects of drought periods on NPP depended on vegetation types and rangeland conditions. Annual plants had the highest drought sensitivity while shrubs exhibited the lowest drought sensitivity. The positive effects of wet periods on NPP were less evident in degraded areas where the destructive effects of drought were more prominent. Therefore, determining vegetation types and rangeland conditions is essential in NPP estimation. The findings of this study confirmed the potential of the CASA for estimating rangeland production. Therefore, the model output maps can be used to evaluate, monitor and optimize rangeland management in semi-arid rangelands of Iran where MODIS NPP products are not available.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Alamdari P, Nematollahi O, Alemrajabi A A. 2013. Solar energy potentials in Iran: A review. Renewable and Sustainable Energy Reviews, 21: 778–788.

    Article  Google Scholar 

  • Ardo J, Tagesson T, Jamali S, et al. 2018. MODIS EVI-based net primary production in the Sahel 2000–2014. International Journal of Applied Earth Observation and Geoinformation, 65: 35–45.

    Article  Google Scholar 

  • Bao G, Bao Y H, Qin Z H, et al. 2016. Modeling net primary productivity of terrestrial ecosystems in the semi-arid climate of the Mongolian Plateau using LSWI-based CASA ecosystem model. International Journal of Applied Earth Observation and Geoinformation, 46: 84–93.

    Article  Google Scholar 

  • Biondini M E, Patton B D, Nyren P E. 1998. Grazing intensity and ecosystem processes in a northern mixed-grass prairie, USA. Ecological Applications, 8(2): 469–479.

    Article  Google Scholar 

  • Bonham C D. Measurement for Terrestrial Vegetation. Hoboken: John Wiley & Sons, 338.

  • Caylor K K, Shugart H H. 2004. Simulated productivity of heterogeneous patches in Southern African savanna landscapes using a canopy productivity model. Landscape Ecology, 19(4): 401–415.

    Article  Google Scholar 

  • Chen G, Tian H, Zhang C, et al. 2012. Drought in the Southern United States over the 20th century: variability and its impacts on terrestrial ecosystem productivity and carbon storage. Climatic Change, 114(2): 379–397.

    Article  Google Scholar 

  • Chen T, Van der Werf G R, De Jeu R A M, et al. 2013. A global analysis of the impact of drought on net primary productivity. Hydrology and Earth System Sciences, 17: 3885–3894.

    Article  Google Scholar 

  • Chen T, Huang Q H, Liu M, et al. 2017. Decreasing net primary productivity in response to urbanization in Liaoning Province, China. Sustainability, 9(2): 162, doi: 10.3390/su9020162.

    Article  Google Scholar 

  • Clark D A, Piper S C, Keeling C D, et al. 2003. Tropical rain forest tree growth and atmospheric carbon dynamics linked to interannual temperature variation during 1984–2000. Proceedings of the National Academy of Sciences of the United States of America, 100(10): 5852–5857.

    Article  Google Scholar 

  • Cuadra S V, Costa M H, Kucharik C J, et al. 2012. Abiophysical model of sugarcane growth. Global Change Biolagy Bioenergy, 4(1): 36–48.

    Article  Google Scholar 

  • Dannenberg M P, Song C, Hwang T, et al. 2015. Empirical evidence of El Nino-Southern Oscillation influence on land surface phenology and productivity in the western United States. Remote Sensing of Environment, 159: 167–180.

    Article  Google Scholar 

  • Dieguez H, Paruelo J M. 2017. Disentangling the signal of climatic fluctuations from land use: changes in ecosystem functioning in South American protected areas (1982–2012). Remote Sensing in Ecology and Conservation, 3(4): 177–189.

    Article  Google Scholar 

  • Dintwe K, Okin G S. 2018. Soil organic carbon in savannas decreases with anthropogenic climate change. Geoderma, 309: 7–16.

    Article  Google Scholar 

  • Dong G T, Bai J, Yang S T, et al. 2015. The impact of land use and land cover change on net primary productivity on China's Sanjiang Plain. Environmental Earth Sciences, 74(4): 2907–2917.

    Article  Google Scholar 

  • Donmez C, Berberoglu S, Curran P J. 2011. Modelling the current and future spatial distribution of NPP in a Mediterranean watershed. International Journal of Applied Earth Observation and Geoinformation, 13(3): 336–345.

    Article  Google Scholar 

  • Feizi M T. 2018. Ecological Regions of Iran Vegetation Types of Isfahan Province. Tehran: Research Institute of Forests and Rengelands, 290.

    Google Scholar 

  • Fischer D G, Chapman S K, Classen A T, et al. 2014. Plant genetic effects on soils under climate change. Plant and Soil, 379(1–2): 1–19.

    Article  Google Scholar 

  • Fridley JD, Lynn J S, Grime J P, et al. 2016. Longer growing seasons shift grassland vegetation towards more-productive species. Nature Climate Change, 6: 865–868.

    Article  Google Scholar 

  • Friedel M H, Shaw K. 1987. Evaluation of methods for monitoring sparse patterned vegetation in arid rangelands. I. Herbage. Journal of Environmental Management, 25(4): 309–318.

    Google Scholar 

  • Friedel M H. 1991. Range condition assessment and the concept of thresholds: A viewpoint. Journal of Range Management, 44(5): 422–426.

    Article  Google Scholar 

  • Gao Q Z, Wan Y F, Li Y, et al. 2013. Effects of topography and human activity on the net primary productivity (NPP) of alpine grassland in northern Tibet from 1981 to 2004. International Journal of Remote Sensing, 34(6): 2057–2069.

    Article  Google Scholar 

  • Goetz S J, Prince S D, Goward S N, et al. 1999. Satellite remote sensing of primary production: an improved production efficiency modeling approach. Ecological Modelling, 122(3): 239–255.

    Article  Google Scholar 

  • Goldsmith F B. 1991. Monitoring for Conservation and Ecology. Virginia Beach: Chapman & Hall Press, 275.

    Book  Google Scholar 

  • Hadian F, Jafari R, Bashari H. 2013. Assessing the accuracy of spectral indices in vegetation cover mapping at vegetation type and across vegetation type scales, using TM sensor data in southern Zagros regions. Iranian Remote Sensing and GIS, 4(4): 83–100.

    Google Scholar 

  • Holechek J L, Pieper R D, Herbel C H. 1989. Range Management. Principles and Practices. New Jersey: Prentice Hall Press, 526.

    Google Scholar 

  • Hua L Z, Liu H, Zhang X L, et al. 2014. Estimation terrestrial net primary productivity based on CASA Model: a case study in Minnan urban agglomeration, China. IOP Conference Series: Earth and Environmental Science. IOP Publishing, 012153, doi: 10.1088/1755-1315/17/1/012153.

    Google Scholar 

  • IRIMO. 2016. Islamic republic of Iran meteorological organization (IRIMO). [2016-06-29]. https://doi.org/www.irimo.ir/eng/index.php.

    Google Scholar 

  • Jafari R, Lewis M M, Ostendorf B. 2007. Evaluation of vegetation indices for assessing vegetation cover in southern arid lands in South Australia. The Rangeland Journal, 29(1): 39–49.

    Article  Google Scholar 

  • Jafari R, Bakhshandehmehr L. 2013. Quantitative mapping and assessment of environmentally sensitive areas to desertification in central Iran. Land Degradation & Development, 27(2): 108–119.

    Article  Google Scholar 

  • Jay S, Potter C, Crabtree R, et al. 2016. Evaluation of modelled net primary production using MODIS and landsat satellite data fusion. Carbon Balance and Management, 11: 8, doi: 10.1186/sl3021-016-0049-6.

    Article  Google Scholar 

  • Ji L, Peters A J. 2003. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 87(1): 85–98.

    Article  Google Scholar 

  • Jiang C, Wu Z F, Cheng J, et al. 2015. Impacts of urbanization on net primary productivity in the Pearl River Delta, China. International Journal of Plant Production, 9(4): 581–598.

    Google Scholar 

  • Kaminski T, Knorr W, Rayner P J, et al. 2002. Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle. Global Biogeochemical Cycles, 16(4): 14–1–14–16.

    Article  Google Scholar 

  • Kehl M. 2009. Quaternary climate change in Iran-the state of knowledge. Erdkunde, 63(1): 1–17.

    Article  Google Scholar 

  • Khajeddin S J. 1995. A survey of the plant communities of the Jazmorian, Iran, using Landsat MSS data. Ph.D Thesis. UK: University of Reading.

    Google Scholar 

  • Khaleghi M R, Aeinebeygi S. 2016. An assessment of biennial enclosure effects on range production, condition and trend (case study: Taftazan rangeland, Shirvan). International Journal of Forest, Soil and Erosion (IJFSE), 6(2): 33–40.

    Google Scholar 

  • Khatibi R, Soltani S, Khodagholi M. 2017. Effects of climatic factors and soil salinity on the distribution of vegetation types containing Anabasis aphylla in Iran: a multivariate factor analysis. Arabian Journal of Geosciences, 10(2): 36, doi: 10.1007/sl2517-016-2812-0.

    Article  Google Scholar 

  • Kimball J S, White M A, Running S W. 1997. BIOME-BGC simulations of stand hydrologic processes for BOREAS. Journal of Geophysical Research, 102(D24): 29043–29051.

    Article  Google Scholar 

  • Lammers J M, Schubert C J, Middelburg J J, et al. 2017. Microbial carbon processing in oligotrophic Lake Lucerne (Switzerland): results of in situ 13C-labelling studies. Biogeochemistry, 136(2): 131–149.

    Article  Google Scholar 

  • Lei T J, Wu J J, Li X H, et al. 2015. A new framework for evaluating the impacts of drought on net primary productivity of grassland. Science of The Total Environment, 536: 161–172.

    Article  Google Scholar 

  • Li W, Huang H Z, Zhang Z N, et al. 2011. Effects of grazing on the soil properties and C and N storage in relation to biomass allocation in an alpine meadow. Journal of Soil Science and Plant Nutrition, 11(4): 27–39.

    Article  Google Scholar 

  • Liang W, Yang Y T, Fan D M, et al. 2015. Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010. Agricultural and Forest Meteorology, 204: 22–36.

    Article  Google Scholar 

  • Lloyd-Hughes B, Saunders M A. 2002. A drought climatology for Europe. International Journal of Climatology, 22(13): 1571–1592.

    Article  Google Scholar 

  • Lokupitiya E, Denning S, Paustian K, et al. 2009. Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands. Biogeosciences, 6(6): 969–986.

    Article  Google Scholar 

  • Lu Q S, Gao Z Q, Ning J C, et al. 2015. Impact of progressive urbanization and changing cropping systems on soil erosion and net primary production. Ecological Engineering, 75: 187–194.

    Article  Google Scholar 

  • McCoy R M. 2005. Field Methods in Remote Sensing. New York: Guilford Press, 575.

    Google Scholar 

  • Nemry B, FrançOis L, Gérard J C, et al. 1999. Comparing global models of terrestrial net primary productivity (NPP): analysis of the seasonal atmospheric CO2 signal. Global Change Biology, 5(S1): 65–76.

    Article  Google Scholar 

  • O'connor T G, Roux P W. 1995. Vegetation changes (1949-71) in a semi-arid, grassy dwarf shrubland in the Karoo, South Africa: influence of rainfall variability and grazing by sheep. Journal of Applied Ecology, 32(3): 612–626.

    Article  Google Scholar 

  • Pan G, Sun G J, Li F M. 2009. Using QuickBird imagery and a production efficiency model to improve crop yield estimation in the semi-arid hilly Loess Plateau, China. Environmental Modelling & Software, 24(4): 510–516.

    Article  Google Scholar 

  • Peng J, Loew A, Zhang S Q, et al. 2015. Spatial downscaling of satellite soil moisture data using a vegetation temperature condition index. Geoscience and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2015.2462074.

    Google Scholar 

  • Potter C S, Klooster S, Brooks V. 1999. Interannual variability in terrestrial net primary production: exploration of trends and controls on regional to global scales. Ecosystems, 2(1): 36–48.

    Article  Google Scholar 

  • Potter C. 2012a. Net primary production and carbon cycling in coast redwood forests of central California. Open Journal of Ecology, 2(3): 147–153.

    Article  Google Scholar 

  • Potter C, Klooster S, Genovese V. 2012b. Net primary production of terrestrial ecosystems from 2000 to 2009. Climatic Change, 115(2): 365–378.

    Article  Google Scholar 

  • Rafique R, Xia J Y, Hararuk O, et al. 2017. Comparing the performance of three land models in global C cycle simulations: A detailed structural analysis. Land Degradation & Development, 28(2): 524–533.

    Article  Google Scholar 

  • Rohli R V, Vega A J. 2013. Climatology. Burlinton: Jones & Bartlett Publishers, 451.

    Google Scholar 

  • Roupsard O, Le Maire G L, Nouvellon Y, et al. 2009. Scaling-up productivity (NPP) using light or water use efficiencies (LUE, WUE) from a two-layer tropical plantation. Agroforestry Systems, 76(2): 409–422.

    Article  Google Scholar 

  • Roxburgh S H, Berry S L, Buckley T N, et al. 2005. What is NPP? Inconsistent accounting of respiratory fluxes in the definition of net primary production. Functional Ecology, 19(3): 378–382.

    Article  Google Scholar 

  • Ruimy A, Saugier B, Dedieu G. 1994. Methodology for the estimation of terrestrial net primary production from remotely sensed data. Journal of Geophysical Research: Atmospheres, 99(D3): 5263–5283.

    Article  Google Scholar 

  • Sainte-Marie J, Henrot A, Barrandon M, et al. 2012. Modeling the environmental and seasonal influence on canopy dynamic and litterfall of even-aged forest ecosystems by a model coupling growth & yield and process-based approaches. In: 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications. Shanghai, China: IEEE, 324–331.

    Chapter  Google Scholar 

  • Schimel D S, Braswell B H, Holland E A, et al. 1994. Climatic, edaphic, and biotic controls over storage and turnover of carbon in soils. Global Biogeochemical Cycles, 8(3): 279–293.

    Article  Google Scholar 

  • Schlesinger W H, Andrews J A. 2000. Soil respiration and the global carbon cycle. Biogeochemistry, 48(1): 7–20.

    Article  Google Scholar 

  • Stephenson N L. 1990. Climatic control of vegetation distribution: the role of the water balance. The American Naturalist, 135(5): 649–670.

    Article  Google Scholar 

  • Stoddart L, Smith A, Box T. 1975. Range Management (3rd ed.). New York: McGraw-Hill Book Company, 433.

    Google Scholar 

  • Sun Z G, Long X H, Sun C M, et al. 2013. Evaluation of net primary productivity and its spatial and temporal patterns in southern China's grasslands. The Rangeland Journal, 35(3): 331–338.

    Article  Google Scholar 

  • Throop H L, Reichmann L G, Sala O E, et al. 2012. Response of dominant grass and shrub species to water manipulation: an ecophysiological basis for shrub invasion in a Chihuahuan Desert grassland. Oecologia, 169(2): 373–383.

    Article  Google Scholar 

  • Wang S Y, Zhang B, Yang Q C, et al. 2017. Responses of net primary productivity to phenological dynamics in the Tibetan Plateau, China. Agricultural and Forest Meteorology, 232: 235–246.

    Article  Google Scholar 

  • Wessels K J, Prince S D, Malherbe J, et al. 2007. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments, 68(2): 271–297.

    Article  Google Scholar 

  • Whitehead D, Gower S T. 2001. Photosynthesis and light-use efficiency by plants in a Canadian boreal forest ecosystem. Tree Physiology, 21(12–13): 925–929.

    Article  Google Scholar 

  • Wilson A D, Abraham N A, Barratt R, et al. 1987. Evaluation of methods of assessing vegetation change in the semi-arid rangelands of southern Australia. The Rangeland Journal, 9(1): 5–13.

    Article  Google Scholar 

  • Xia J Y, Luo Y Q, Wang Y P, et al. 2013. Traceable components of terrestrial carbon storage capacity in biogeochemical models. Global Change Biology, 19(7): 2104–2116.

    Article  Google Scholar 

  • Xu X, Sherry R A, Niu S L, et al. 2013. Net primary productivity and rain-use efficiency as affected by warming, altered precipitation, and clipping in a mixed-grass prairie. Global Change Biology, 19(9): 2753–2764.

    Article  Google Scholar 

  • Yeganeh H, Khajedein S J, Amiri F, et al. 2014. Monitoring rangeland ground cover vegetation using multitemporal MODIS data. Arabian Journal of Geosciences, 7(1): 287–298.

    Article  Google Scholar 

  • Yu D Y, Yang M C, Pan Y Z, et al. 2005. Study on temporal and spatial changes of light utilization efficiency (LUE) for vegetations in Eastern Asia. In: 2005 IEEE Geoscience and Remote Sensing Symposium, 2005. IGARSS'05. Seoul, South Korea: IEEE, 1896–1899.

    Google Scholar 

  • Yu D Y, Shi P J, Shao H B, et al. 2009. Modelling net primary productivity of terrestrial ecosystems in East Asia based on an improved CASA ecosystem model. International Journal of Remote Sensing, 30(18): 4851–4866.

    Article  Google Scholar 

  • Yu R, Evans A J, Malleson N. 2018. Quantifying grazing patterns using a new growth function based on MODIS Leaf Area Index. Remote Sensing of Environment, 209: 181–194.

    Article  Google Scholar 

  • Yuan J G, NIU Z, WANG C L. 2006. Vegetation NPP distribution based on MODIS data and CASA Model—a case study of northern Hebei Province. Chinese Geographical Science, 16(4): 334–341.

    Article  Google Scholar 

  • Zhang L, Zhou G S, Ji Y H, et al. 2017. Grassland carbon budget and its driving factors of the subtropical and tropical monsoon region in China during 1961 to 2013. Scientific Reports, 7: 14717, doi: 10.1038/s41598-017-15296-7.

    Article  Google Scholar 

  • Zhang L X, Zhou D C, Fan J W, et al. 2015. Comparison of four light use efficiency models for estimating terrestrial gross primary production. Ecological Modelling, 300: 30–39.

    Article  Google Scholar 

  • Zhang N, Yu Z L, Yu G R, et al. 2007. Scaling up ecosystem productivity from patch to landscape: a case study of Changbai Mountain Nature Reserve, China. Landscape Ecology, 22(2): 303–315.

    Article  Google Scholar 

  • Zhou W, Gang C C, Zhou F C, et al. 2015. Quantitative assessment of the individual contribution of climate and human factors to desertification in northwest China using net primary productivity as an indicator. Ecological Indicators, 48: 560–569.

    Article  Google Scholar 

  • Zika M, Erb K H. 2009. The global loss of net primary production resulting from human-induced soil degradation in drylands. Ecological Economics, 69(2): 310–318.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Natural Resources, Isfahan University of Technology, Isfahan, 8415683111, Iran

    Fatemeh Hadian, Reza Jafari, Hossein Bashari & Mostafa Tartesh

  2. School of Biological Sciences, University of Adelaide, South Australia, 5005, Australia

    Kenneth D. Clarke

Authors
  1. Fatemeh Hadian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Jafari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hossein Bashari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mostafa Tartesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kenneth D. Clarke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Jafari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hadian, F., Jafari, R., Bashari, H. et al. Estimation of spatial and temporal changes in net primary production based on Carnegie Ames Stanford Approach (CASA) model in semi-arid rangelands of Semirom County, Iran. J. Arid Land 11, 477–494 (2019). https://doi.org/10.1007/s40333-019-0060-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40333-019-0060-3

Keywords

Search

Navigation