Advertisement

The start and end of the growing season in Pakistan during 1982–2015

  • Sarfaraz Ali Bhutto
  • Xiaoyue WangEmail author
  • Jian Wang
Original Article
  • 63 Downloads

Abstract

Phenology plays an important role in plant productivity and ecosystem function. Long-term observations of crop phenology are being used to track crop responses to climate change. In this paper, a dataset of 34 years (1982–2015) of biweekly advanced very high-resolution radiometer (AVHRR) normalized difference vegetation index, third generation (NDVI3g), measurements was used to examine the spatial and temporal patterns of crops. Phenological aspects such as the start of the season (SOS), the end of the season (EOS), and their variability were investigated using the NDVI time series for Pakistan. The quantitative effects of climate variables, namely, temperature and precipitation, were analyzed to determine the seasonal variability in crop phenology. The study indicates a large spatial and temporal variation in the different phenological characteristics of crops within the country throughout the study period. The length of the growing season coincides well with the onset and withdrawal of monsoons; a high positive correlation in the moist northern part of the study area was observed, whereas the semiarid areas in the south exhibited a small positive correlation. The temperature in the spring primarily controlled early SOS, and warmer autumn temperature delayed EOS. The results indicate that the crops were influenced by climate. Crop monitoring on a spatial scale is essential to developing outcomes and reducing exposure risks in the future.

Keywords

Crop phenology NDVI3g Climate controls Spatiotemporal patterns Pakistan 

Notes

Acknowledgements

This work was funded by the national Key R&D program of China (2018YFA0606101), the National Natural Science Foundation of China (41871255), the Key Research Program of Frontier Sciences, CAS to C. Wu (QYZDB-SSW-DQC011), and the Food and Agriculture Organization (FAO) in coordination with the Space and Upper Atmosphere Research Commission (SUPARCO) of Pakistan and the Crop Reporting Service Centre (CRSC), Sindh, Pakistan, under the project Monitoring of crops through satellite technology Phase-II (UTF/PAK/101/PAK).

References

  1. Aggarwal PK, Sivakumar VK (2011) Global climate change and food security in South Asia: an adaptation and mitigation framework. In Climate change and food security in South Asia, pp 253–75Google Scholar
  2. Aguilar C, Zinnert JC, Polo MJ, Young DR (2012) NDVI as an indicator for changes in water availability to woody vegetation. Ecol Ind 23:290–300CrossRefGoogle Scholar
  3. Ali A, Erenstein O (2016) Assessing farmer use of climate change adaptation practices and impacts on food security and poverty in Pakistan. Clim Risk Manag 16:183–194CrossRefGoogle Scholar
  4. Bhatti AM, Suttinon P, Nasu S (2009) Agriculture water demand management in pakistan: a review and perspective. Soc Soc Manag Syst 9(172):1–7Google Scholar
  5. Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B, Eklundh L (2004) A simple method for reconstructing a high-quality NDVI time-series data set based on the savitzky-golay filter. Remote Sens Environ 91(3–4):332–344CrossRefGoogle Scholar
  6. Chen B, Xu G, Coops NC, Ciais P, Innes JL, Wang G, Myneni RB et al (2014) Changes in vegetation photosynthetic activity trends across the Asia-Pacific region over the last three decades. Remote Sens of Environ 144:28–41CrossRefGoogle Scholar
  7. Chmielewski FM, Antje M, Ekko B (2004) Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961–2000. Agric For Meteorol 121(1–2):69–78CrossRefGoogle Scholar
  8. Climate, Pakistan (2013) Climate, average weather of pakistan. http://www.pakistan.climatemps.com/. Accessed Jan 2018
  9. de Beurs KM, Henebry GM (2004) Land surface phenology, climatic veriation, and institutional change: analyzing agricultural land cover change in Kazakhstan. Remote Sens Environ 89:497–509CrossRefGoogle Scholar
  10. FAS (2011) Wheat, data and analysis. USDA. https://www.fas.usda.gov/commodities/wheat. Accessed Dec 2017
  11. Fischlin A, Midgley GF, Price JT, Leemans R, Gopal B, Turley C, Rounsevell MDA, Dube OP, Tarazona J, Velichko AA (2007) Ecosystems, their properties, goods and services. Change 48(3):211–272Google Scholar
  12. Friedl MH, Henebry GM, Reed BC, Huete A, White MA, Morisette V, Nemani RR, Zhang X, Myneni RB, Friedl M (2006) Land surface phenology. A community white paper requested by NASA April 10Google Scholar
  13. Friedl MA, Damien S-M, Bin T, Annemarie S, Navin R, Adam S, Xiaoman H (2010) MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ 114(1):168–182 (Elsevier Inc)CrossRefGoogle Scholar
  14. Garonna I, de Jong R, de Wit AJW, Mücher CA, Schmid B, Schaepman ME (2014) Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982–2011). Glob Change Biol 20(11):3457–3470CrossRefGoogle Scholar
  15. Gonsamo A, Chen JM, Wu C, Dragoni D (2012) Predicting deciduous forest carbon uptake phenology by upscaling fluxnet measurements using remote sensing data. Agric For Meteorol 165:127–135 (Elsevier B.V.)CrossRefGoogle Scholar
  16. Gourdji SM, Adam M, Sibley, Lobell DB (2013) Global crop exposure to critical high temperatures in the reproductive period: historical trends and future projections. Environ Res Lett 8(2)Google Scholar
  17. Government of Pakistan (2017) Chap. 02, Agriculture 2016–2017. pp 19–40. http://www.finance.gov.pk/survey/chapters_17/02-Agriculture.pdf. Accessed Jan 2018
  18. Hussain SS, Mudasser M (2007) Prospects for wheat production under changing climate in mountain areas of pakistan—an econometric analysis. Agric Syst 94(2):494–501CrossRefGoogle Scholar
  19. Jeganathan C, Dash J, Atkinson PM (2014) Remotely sensed trends in the phenology of northern high latitude terrestrial vegetation, controlling for land cover change and vegetation type. Remote Sens Environ 143:154–170CrossRefGoogle Scholar
  20. Jeong SJ, Ho CH, Gim HJ, Brown ME (2011) Phenology shifts at start vs. end of growing season in temperate vegetation over the northern hemisphere for the period 1982–2008. Glob Change Biol 17(7):2385–2399CrossRefGoogle Scholar
  21. Knohl A, Schulze ED, Kolle O, Buchmann N (2003) Large carbon uptake by an unmanaged 250-year-old deciduous forest in central Germany. Agric For Meteorol 118(3–4):151–167CrossRefGoogle Scholar
  22. Knox J, Hess T, Daccache A, Wheeler T (2012) Climate change impacts on crop productivity in Africa and south Asia. Environ Res Lett 7(3):034032CrossRefGoogle Scholar
  23. Lobell DB, Asner GP (2003) Climate and management contributions to recent trends in U.S. agricultural yields. Science 299(5609):1032CrossRefGoogle Scholar
  24. Lobell DB, Burke MB, Tebaldi C, Mastrandrea MD, Falcon WP, Naylor RL (2008) Prioritizing climate change adaptation needs for food security in 2030. Science 319(5863):607–610CrossRefGoogle Scholar
  25. Melaas EK, Friedl MA, Zhu Z (2013) Detecting interannual variation in deciduous broadleaf forest phenology using landsat TM/ETM+ data. Remote Sens Environ 132:176–185 (Elsevier Inc.)CrossRefGoogle Scholar
  26. Menzel A, Sparks TH, Estrella N, Koch E, Aaasa A, Ahas R, Alm-Kübler K et al (2006) European phenological response to climate change matches the warming pattern. Glob Change Biol 12(10):1969–1976CrossRefGoogle Scholar
  27. Mitigate TO (2011) Cotton and climate change 33Google Scholar
  28. Morisette JT, Richardson AD, Knapp AK, Fisher JI, Graham EA, Abatzoglou J, Wilson BE et al (2009) Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front Ecol Environ 7(5):253–260CrossRefGoogle Scholar
  29. Myneni RBB, Keeling CDD, Tucker CJJ, Asrar G, Nemani RR (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386(6626):698–702CrossRefGoogle Scholar
  30. Ogutu B, Dash J, Dawson TP (2014) Evaluation of leaf area index estimated from medium spatial resolution remote sensing data in a broadleaf deciduous forest in southern England, UK. Can J Remote Sens 37(4):333–347CrossRefGoogle Scholar
  31. Ouyang W, Hao F, Skidmore AK, Groen TA, Toxopeus AG, Wang T (2012) Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed. Ecol Indic 18:163–170CrossRefGoogle Scholar
  32. Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Ann Rev Ecol Evolution Syst 37(1):637–669CrossRefGoogle Scholar
  33. Peng S, Huang J, Sheehy JE, Laza RC, Visperas RM, Zhong X, Centeno GS, Khush GS, Cassman KG (2004) Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci 101(27):9971–9975CrossRefGoogle Scholar
  34. Piao S, Fang J, Zhou L, Ciais P, Zhu B (2006) Variations in satellite-derived phenology in china’s temperate vegetation. Glob Change Biol 12(4):672–685CrossRefGoogle Scholar
  35. Piao S, Ciais P, Huang Y, Shen Z, Peng S, Li J, Zhou L et al (2010) The impacts of climate change on water resources and agriculture in China. Nature 467(7311):43–51CrossRefGoogle Scholar
  36. Piao S, Wang X, Ciais P, Zhu B, Wang T, Liu J (2011) Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Glob Change Biol 17(10):3228–3239CrossRefGoogle Scholar
  37. Rana MA (2013) Exploring dynamics of cotton seed provision in Sindh. No. FebruaryGoogle Scholar
  38. Ray DK, Gerber JS, Macdonald GK, West PC (2015) Climate variation explains a third of global crop yield variability. Nat Commun 6:5989CrossRefGoogle Scholar
  39. Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013) Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol 169:156–173 (Elsevier B.V.)CrossRefGoogle Scholar
  40. Root TL, Price JT, Hall KR, Schneider SH (2003) Fingerprints of global warming on wild animals and plants. Nature 421(6918):57–60CrossRefGoogle Scholar
  41. Sarwar M, Ajmal MK, Iqbal Z (2002) Feed resources for livestock in Pakistan. Int J Agric Biol 4(1):186–192Google Scholar
  42. Shen M (2011) Spring phenology was not consistently related to winter warming on the Tibetan Plateau. Proc Natl Acad Sci U S A 108(19):E91–E92 (author reply E95)CrossRefGoogle Scholar
  43. Shen M, Tang Y, Chen J, Zhu X, Zheng Y (2011) Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau. Agric For Meteorol 151(12):1711–1722 (Elsevier B.V.)CrossRefGoogle Scholar
  44. Shen M, Zhang G, Cong N, Wang S, Kong W, Piao S (2014) Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai-Tibetan Plateau. Agric For Meteorol 189–190:71–80 (Elsevier B.V)CrossRefGoogle Scholar
  45. Sonnentag O, Hufkens K, Teshera-Sterne C, Young AM, Friedl M, Braswell BH, Milliman T, O’Keefe J, Richardson AD (2012) Digital repeat photography for phenological research in forest ecosystems. Agric For Meteorol 152(1):159–177CrossRefGoogle Scholar
  46. Sultan S, Wu R, Ahmed I (2014) Impact of terrain and cloud cover on the distribution of incoming direct solar radiation over Pakistan. J Geogr Inform Syst 2014(6):70–77Google Scholar
  47. Sultana H, Ali N, Iqbal MM, Khan AM (2009) Vulnerability and adaptability of wheat production in different climatic zones of Pakistan under climate change scenarios. Clim Change 94(1–2):123–142CrossRefGoogle Scholar
  48. Tans P, Dlugokencky E (2013) National oceanic and atmospheric administration earth system research laboratory. Trends Atmos Carbon Dioxide. http://www.esrl.noaa.gov/gmd/ccgg/trends/global.html. Accessed Jan 2018
  49. Vitasse Y, François C, Delpierre N, Dufrêne E, Kremer A, Chuine I, Delzon S (2011) Assessing the effects of climate change on the phenology of European temperate trees. Agric For Meteorol 151(7):969–980CrossRefGoogle Scholar
  50. Wang X, Piao S, Ciais P, Li J, Friedlingstein P, Koven C, Chen A (2011) Spring temperature change and its implication in the change of vegetation growth in north America from 1982 to 2006. Proc Natl Acad Sci USA 108(4):1240–1245CrossRefGoogle Scholar
  51. White MA, de Beurs KM, Didan K, Inouye DW, Richardson AD, Jensen OP, O’Keefe J et al (2009) Intercomparison, interpretation, and assessment of spring phenology in north America estimated from remote sensing for 1982–2006. Glob Change Biol 15(10):2335–2359CrossRefGoogle Scholar
  52. Wu C, Chen JM, Black TA, Price DT, Kurz WA, Desai AR, Gonsamo A et al (2013) Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn. Glob Ecol Biogeogr 22(8):994–1006CrossRefGoogle Scholar
  53. Wu C, Gonsamo A, Gough CM, Chen JM, Xu S (2014) Modeling growing season phenology in north American forests using seasonal mean vegetation indices from MODIS. Remote Sens Environ 147:79–88 (Elsevier Inc.)CrossRefGoogle Scholar
  54. Wu C, Hou X, Peng D, Gonsamo A, Xu S (2016) Land surface phenology of China’s temperate ecosystems over 1999–2013: spatial-temporal patterns, interaction effects, covariation with climate and implications for productivity. Agric For Meteorol 216:177–187 (Elsevier B.V.)CrossRefGoogle Scholar
  55. Xu L, Myneni RB, Chapin FS III, Callaghan TV, Pinzon JE, Tucker CJ, Zhu Z et al (2013) Temperature and vegetation seasonality diminishment over northern lands. Nat Clim Change 3(3):581–586CrossRefGoogle Scholar
  56. Yu H, Luedeling E, Xu J (2010) Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Pnas 107(51):22151–22156CrossRefGoogle Scholar
  57. Zhang X, Goldberg MD (2011) Monitoring fall foliage coloration dynamics using time-series satellite data. Remote Sens Environ 115(2):382–391CrossRefGoogle Scholar
  58. Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, Reed BC, Huete A (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84(3):471–475CrossRefGoogle Scholar
  59. Zhang G, Zhang Y, Dong J, Xiao X (2013) Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proc Natl Acad Sci 110(11):4309–4314CrossRefGoogle Scholar
  60. Zhu W, Tian H, Xu X, Pan Y, Chen G, Lin W (2012) Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982–2006. Glob Ecol Biogeogr 21(2):260–271CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Sarfaraz Ali Bhutto
    • 1
    • 2
  • Xiaoyue Wang
    • 3
    Email author
  • Jian Wang
    • 1
    • 2
  1. 1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.University of the Chinese Academy of SciencesBeijingChina
  3. 3.The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

Personalised recommendations