Analyzing the Impact of Spatio-Temporal Climate Variations on the Rice Crop Calendar in Pakistan

Authors: Muhammad Imran, Iqra Basit, Mobushir Riaz Khan, Sajid Rasheed Ahmad

Abstract:

The present study investigates the space-time impact of climate change on the rice crop calendar in tropical Gujranwala, Pakistan. The climate change impact was quantified through the climatic variables, whereas the existing calendar of the rice crop was compared with the phonological stages of the crop, depicted through the time series of the Normalized Difference Vegetation Index (NDVI) derived from Landsat data for the decade 2005-2015. Local maxima were applied on the time series of NDVI to compute the rice phonological stages. Panel models with fixed and cross-section fixed effects were used to establish the relation between the climatic parameters and the time-series of NDVI across villages and across rice growing periods. Results show that the climatic parameters have significant impact on the rice crop calendar. Moreover, the fixed effect model is a significant improvement over cross-sectional fixed effect models (R-squared equal to 0.673 vs. 0.0338). We conclude that high inter-annual variability of climatic variables cause high variability of NDVI, and thus, a shift in the rice crop calendar. Moreover, inter-annual (temporal) variability of the rice crop calendar is high compared to the inter-village (spatial) variability. We suggest the local rice farmers to adapt this change in the rice crop calendar.

Keywords: Landsat NDVI, panel models, temperature, rainfall.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1317168

References:

[1] IPCC, 2007. IPCC Fourth Assessment Report: Climate Change 2007 (AR4). IPCC, Geneva, Switzerland.
[2] Ramirez-Villegas, J., A. Jarvis and P. Laderach, 2011. Empirical approaches for assessing impacts of climate change on agriculture: The EcoCrop model and a case study with grain sorghum. Agricultural and Forest Meteorology, 12:67-78
[3] Abou-Shleel, S. M. and M. A. El-Shirbeny, 2014. GIS Assessment of Climate Change Impacts on Tomato Crop in Egypt. Global Journal of Environmental Research, 8(2): 26-34.
[4] Brown, M., K. Beurs and M. Marshall, 2012. Global phenological response to climate change in crop areas using satellite remote sensing of vegetation, humidity and temperature over 26 years. Remote Sensing of Environment. 126: 174–183.
[5] Kotsuki, S. and K. Tanaka, 2015. SACRA – a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI. Hydrology and Earth System Sciences, 19(11): 4441–4461.
[6] Lauxa, P., G. Jackel, R. M. Tingem and H. Kunstmanna, 2010. Impact of climate change on agricultural productivity under rainfed conditions in Cameroon — A method to improve attainable crop yields by planting date adaptations. Agricultural and Forest Meteorolog, 150: 1258–1271.
[7] Rowhani, P. B., D. Lobellb and M. Linderma, 2011. Climate variability and crop production in Tanzania. Agricultural and Forest Meteorology. 151: 449–460.
[8] Thornton, P. K., P. G. Jones, P. J. Ericksen and A. J. Challinor, 2011. Agriculture and food systems in sub-Saharan Africa in a 4˚C+ world. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 369: 117–136.
[9] Waha, K., C. Muller, A. Bondeau, J. Dietrich, P. Kurukulasuriya, J. Heinke and H. Lotze-Campen, 2013. Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa. Global Environmental Change, 23:130–143.
[10] Battisti, D. S. and R. L. Naylor, 2009. Historical warnings of future food insecurity with unprecedented seasonal heat. Science, 323(5911):240–244.
[11] Rosenzweig, C. and D. Hillel, 1998. Climate Change and the Global Harvest: Potential Impacts of the Greenhouse Effect on Agriculture. Oxford University Press.
[12] Wang, H., H. Lin, D. K. Munroe, X. Zhang and P. Liu, 2016. Reconstructing Rice Phenology Curves with Frequency-Based Analysis And Multi-Temporal NDVI In Double-Cropping Area In Jiangsu, China. Front. Earth Sci, 10(2): 292–302.
[13] Wang, H., J. Chen, Z. Wu and H. Lin, 2012. Rice heading date retrieval based on multitemporal MODIS data and polynomial fitting. International Journal of Remote Sensing, 33(6): 905–1916.
[14] Propastin, P. A. and M. Kappas, 2008. Reducing Uncertainty in Modeling the NDVI-Precipitation Relationship: A Comparative Study. GIScience & Remote Sensing, 45(1): 47–67.
[15] Vijayan, L. and F. Talhi. 2015. Significance of Meteorological Parameters in the Implementation of Agriculture Engineering Practices in and Around Tabuk Region, KSA. International Journal of Applied Science and Technology, 3(5): 53-65.
[16] Camberlin, P., N. Martiny, N. Philippon and Y. Richard, 2007. Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa. Remote Sensing of Environmen, 106(2): 199-216.
[17] Foody, G. M., 2003. Geographical weighting as a further refinement to regression modelling: an example focused on the NDVI–rainfall relationship. Remote sensing of Environment, 88(3): 283-293.
[18] Gurung, R. B., F. J. Breidt, A. Dutin and S. M. Ogle, 2009. Predicting enhanced vegetation index (EVI) curves for ecosystem modeling applications. Remote Sensing of Environment, 113(10): 2186-2193.
[19] Fabricante, I., Oesterheld, M., and Paruelo, J. M., 2009. Annual and seasonal variation of NDVI explained by current and previous precipitation across Northern Patagonia. Journal of Arid Environments, 73(8): 745-753
[20] Seto, K. C. and R. K. Kaufmann, 2003. Modeling the drivers of urban land use change in the Pearl River Delta, China: integrating remote sensing with socioeconomic data. Land Economics, 79(1): 106-121.
[21] Wassmann, R., S. V. K. Jagadish, K. Sumfleth, H. Pathak, G. Howell, A. Ismail, R. Serraj, E. Redona, R. K. Singh and S. Heuer, 2009. Regional vulnerability of climate change impacts on Asian rice production and scope for adaptation. Advances in Agronomy, 102, pp.91-133.
[22] PMD, 2016. Climate and Astronomical Data, Punjab Meteorological Department (PMD), URL:http://www.pmd.gov.pk/, accessed on June, 2016.
[23] USGS, 2016. Landsat collection. Technical Report. United States Geological Survey (USGS).
[24] SUPARCO, 2016. Crop Calendar. Data, Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), URL:http://suparco.gov.pk, accessed on June, 2016.
[25] DIVA-GIS, URL http://www.diva-gis.org/gdata Accessed on 21 June 2017.
[26] Chen, C., C. Chen and N. T. Son, 2012. Detecting Rice Crop Phenology from Time-Series MODIS Data. The 33rd Asian Conference on Remote Sensing.
[27] Pratt, W. K., 2007. Digital Image Processing. John Wiley & Sons, New York.
[28] Usman, U., S. A. Yelwa, S. Gulumbe and A. Danbaba, 2013. Modelling Relationship between NDVI and Climatic Variables Using Geographically Weighted Regression. Journal of Mathematical Sciences and Application, 1(2): 24-28.
[29] Lopresti, M. F., C. M. Bella and A. J. Degioanni, 2015. Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina. Information Processing in Agriculture, 2(2): 73–84.
[30] Sakamoto, T., N. V. Nguyen, H. Ohno, N. Ishitsuka and M. Yokozawa, 2006. Spatio–temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sensing of Environment. 100: 1-16.
[31] Sakamoto, T., M. Yokozawa, H. Toritani, M. Shibayama, N. Ishitsuka and H. Ohno, 2005. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96: 366 – 374.
[32] Jacoby, W. G., 1998. Statistical Graphics for Visualizing Multivariate Data (Vol. 120).
[33] Wheeler, D. C. and C. A. Cadler, 2007. An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. J. Geogr. Syst, 9: 145-166.
[34] Yan, X., 2009. Linear Regression Analysis: Theory and Computing. World Scientific.
[35] Li, S., Y. Xie, D. G. Brown, Y. Bai, J. Hua and K. Judd, 2013. Spatial variability of the adaptation of grassland vegetation to climatic change in Inner Mongolia of China. Applied Geography, 43: 1-13.
[36] Croissant, Y. and G. Millo, 2008. Panel data econometrics in R: The plm package. Journal of Statistical Software, 27(2), 1-43.
[37] Tao, F., M. Yokozawa, Y. Xu, Y. Hayashi and Z. Zhang, 2006. Climate changes and trends in phenology and yields of field crops in China, 1981–2000. Agricultural and Forest Meteorology, 138(1): 82-92.
[38] De Beurs, K. M. and G. M. Henebry, 2005. A statistical framework for the analysis of long image time series. International Journal of Remote Sensing, 26(8): 1551-1573.
[39] Yegbemey, R. N., H. Kabir, O. H. Awoye, J. A Yabi and A. A. Paraïso, 2014. Managing the agricultural calendar as coping mechanism to climate variability: A case study of maize farming in northern Benin, West Africa. Climate Risk Management, 3:13-23.
[40] Sacks, J. W., D. Deryng, A. Foley and N. Ramankutty, 2010. Crop planting dates: an analysis of global patterns. Global Ecology and Biogeography, 19:607–620.