Abstract
The virtual certainty of the anticipated climate change will continue to raise many questions about its aggregated impact of environmental changes on our regional food security in imminent future. Crop responses to these changes are certain, but its exact characteristics are hardly understood at regional scale due to complex overlapping effects of climate change and anthropogenic manipulation of agro-ecosystem. This study derived phenology of wheat in north India from satellite data and analyzed trends of phenology parameters over last three decades. The most striking change-point period in phenology trends were also derived. The phenology was derived from two sources: (1) STAR-Global vegetation Health Products-NDVI, and (2) GIMMS-NDVI. The results revealed significant earliness in start of growing season (SOS) in Punjab and Haryana while delay was found in Uttar Pradesh (UP). End of the wheat season almost always occurred early, to even those place where SOS was delayed. Length of growing season increased in most of Punjab and northern Haryana whereas its decrease dominated in UP. The early sowing practice of the farmers of the Punjab and Haryana may be one of the adaptation strategies to manage the terminal heat stress in reproductive stage of the crop in the region. The change-point occurred in late 1990s (1998–2000) in Punjab and Haryana, while in eastern UP it was in early 1990s (1990–1995). Despite the difference in temporal aggregation and spatial resolution, both the datasets yielded similar trends, confirming both the robustness of the results and applicability of the datasets over the region. The results demands further research for proper attribution of the effects into its causes and may help devising crop adaption practices to climatic stresses.
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References
Aggarwal, P., Joshi, P. K., Ingram, J., & Gupta, R. (2004). Adapting food systems of the Indo-Gangetic plains to global environmental change: Key information needs to improve policy formulation. Environmental Science & Policy, 7(6), 487–498.
Annual Reports of All India Coordinated Research Project (AICRP) on Agricultural Meteorology, Hisar Centre. 2010–2013.
Chakraborty, A., Seshasai, M. V., & Dadhwal, V. K. (2014). Geo-spatial analysis of the temporal trends of kharif crop phenology metrics over India and its relationships with rainfall parameters. Environmental Monitoring and Assessment, 186(7), 4531–4542. doi:10.1007/s10661-014-3717-6.
Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A., & Schwartz, M. D. (2007). Shifting plant phenology in response to global change. Trends in Ecology & Evolution, 22(7), 357–365.
Dhakar, R., Sehgal, V. K., & Pradhan, S. (2013). Study on inter-seasonal and intra-seasonal relationships of meteorological and agricultural drought indices in the Rajasthan State of India. Journal of Arid Environments, 97, 108–119.
Duncan, J., Dash, J., & Atkinson, P. M. (2015). Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing. Global Change Biology, 21(4), 1541–1551.
Heumann, B. W., Seaquist, J., Eklundh, L., & Jönsson, P. (2007). AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982–2005. Remote Sensing of Environment, 108(4), 385–392.
Jeganathan, C., Dash, J., & Atkinson, P. M. (2014). Remotely sensed trends in the phenology of northern high latitude terrestrial vegetation, controlling for land cover change and vegetation type. Remote Sensing of Environment, 143, 154–170. doi:10.1016/j.rse.2013.11.020.
Jiang, L., Tarpley, J. D., Mitchell, K. E., Zhou, S., Kogan, F. N., & Guo, W. (2008). Adjusting for long-term anomalous trends in NOAA’s global vegetation index data sets. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 409–422.
Jönsson, P., & Eklundh, L. (2002). Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40(8), 1824–1832.
Jönsson, P., & Eklundh, L. (2003). Seasonality extraction from satellite sensor data. In C. H. Chen (Ed.), Frontiers of Remote Sensing Information Processing (pp. 487–500). World Scientific Publishing.
Jönsson, P., & Eklundh, L. (2004). TIMESAT—A program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30(8), 833–845.
Kingra, P. K., Mahey, R. K., Gill, K. K., & Singh, S. (2011). Thermal requirement and heat use efficiency of wheat under different irrigation levels in Central Punjab. Indian Journal of Ecology, 38(2), 228–233.
Lobell, D. B., Ortiz-Monasterio, J. I., Sibley, A. M., & Sohu, V. (2013). Satellite detection of earlier wheat sowing in India and implications for yield trends. Agricultural Systems, 115, 137–143.
Lobell, D. B., Sibley, A., & Ortiz-Monasterio, J. I. (2012). Extreme heat effects on wheat senescence in India. Nature Climate Change, 2(3), 186–189.
Monfreda, C., Ramankutty, N., & Foley, J. A. (2008). Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles, 22(1), GB1022. doi:10.1029/2007GB002947.
Morisette, J. T., Richardson, A. D., Knapp, A. K., Fisher, J. I., Graham, E. A., Abatzoglou, J., et al. (2009). Tracking the rhythm of the seasons in the face of global change: Phenological research in the 21st century. Frontiers in Ecology and the Environment, 7(5), 253–260.
Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G., & Nemani, R. R. (1997). Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386(6626), 698–702. doi:10.1038/386698a0.
Pal, I., & Al-Tabbaa, A. (2011). Assessing seasonal precipitation trends in India using parametric and non-parametric statistical techniques. Theoretical and Applied Climatology, 103(1–2), 1–11.
Pettitt, A. (1979). A non-parametric approach to the change-point problem. Applied Statistics, 28(2), 126–135.
Piao, S., Fang, J., Zhou, L., Ciais, P., & Zhu, B. (2006). Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology, 12(4), 672–685.
Rang, A., Mangat, G., & Kaur, R. (2011). Status paper on rice in Punjab. Hyderabad: Rice Knowledge Management Portal (RKMP).
R Core Team. (2016). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org/.
R Studio Team. (2015). RStudio: Integrated development for R. Boston, MA: RStudio, Inc. http://www.rstudio.com/.
Sehgal, V. K., Jain, S., Aggarwal, P. K., & Jha, S. (2011). Deriving crop phenology metrics and their trends using times series NOAA-AVHRR NDVI data. Journal of the Indian Society of Remote Sensing, 39(3), 373–381. doi:10.1007/s12524-011-0125-z.
Singh, R., Oza, S., & Pandya, M. (2006). Observing long-term changes in rice phenology using NOAA-AVHRR and DMSP-SSM/I satellite sensor measurements in Punjab, India. Current Science-Bangalore, 91(9), 1217.
Srivastava, A. K., Rajeevan, M., & Kshirsagar, S. R. (2009). Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmospheric Science Letters, 10(4), 249–254.
Stöckli, R., & Vidale, P. L. (2004). European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. International Journal of Remote Sensing, 25(17), 3303–3330.
Thenkabail, P. S., Knox, J. W., Ozdogan, M., Gumma, M. K., Congalton, R. G., Wu, Z., et al. (2012). Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help? Photogrammetric Engineering and Remote Sensing, 78(8), 773–782.
Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., et al. (2005). An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26(20), 4485–4498.
White, M. A., de Beurs, K. M., Didan, K., Inouye, D. W., Richardson, A. D., Jensen, O. P., et al. (2009). Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Global Change Biology, 15(10), 2335–2359. doi:10.1111/j.1365-2486.2009.01910.x.
Zarenistanak, M., Dhorde, A. G., & Kripalani, R. (2014). Trend analysis and change point detection of annual and seasonal precipitation and temperature series over southwest Iran. Journal of Earth System Science, 123(2), 281–295.
Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., & Myneni, R. B. (2001). Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research: Atmospheres, 106(D17), 20069–20083.
Acknowledgements
First author acknowledges the fellowship provided by the Council for Scientific and Industrial Research (CSIR) during his Ph.D. programme and study leave granted by his employer, ICAR Research Complex for NEH Region, Umiam, Meghalaya. Authors acknowledge support received from IARI in-house Project Grant IARI:NRM:14:(04) and ICAR funded National Innovations in Climate Resilient Agriculture (NICRA) Project. The authors are also thankful to the two anonymous reviewers for their constructive suggestions which have helped in improvement of the analysis along with the manuscript writing.
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Chakraborty, D., Sehgal, V.K., Dhakar, R. et al. Trends and Change-Point in Satellite Derived Phenology Parameters in Major Wheat Growing Regions of North India During the Last Three Decades. J Indian Soc Remote Sens 46, 59–68 (2018). https://doi.org/10.1007/s12524-017-0684-8
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DOI: https://doi.org/10.1007/s12524-017-0684-8