Statistical Analysis Of Impact Of Climate Change On Crop Potentials Productivity On A Regional Scale In Nigeria

Statistical Analysis Of Impact Of Climate Change On Crop Potentials Productivity On A Regional Scale In Nigeria

1*K.O. Rauff, 1A.A. Ismail
*Federal University of Kashere, Gombe State Nigeria
1School of Physics, Universiti Sains Malaysia, 11800 USM, Penang

American Journal of Agricultural Research

Yield improvement is the main aim of all agricultural activities. Therefore, it is important to have an idea about the yield that can be produced from a piece of land before investing in it. This work is aimed at analysing the impact of climate change on crop yield potential and predicting the crop yield potential in six geo political zones in Nigeria using global solar radiation as the only limiting factors of production. Climatic data were obtained from Nigeria Meteorological Agency (NIMET), Oshodi, Nigeria. Results of impact of climate change on the photosynthetic, light-temperature, and climatic potential productivities of maize and their gap differences are presented using a crop growth dynamics statistical method. The results showed that photosynthetic potential productivity decreased from north to south, with the largest values in two maize-growing zones due to higher average growing season radiation and a longer maize growing season. The light-temperature potential productivity of maize was higher than photosynthetic potential productivity, which varied from 3223.99 to 4425.79 kg ha1, with a mean of 3821.402 kg ha1 and climatic potential productivity varied from 11279.92 to 29263.75 kg ha1, with a similar distribution pattern to light-temperature potential productivity with a mean of 23817.32 kg ha1. The gap between light temperature and climatic potential productivity varied from 6884.07 to 33506.92 kg ha 1, with the high value areas centered in Southern Nigeria.

Keywords: Climate Change; Crop yield Potential; Global Solar Radiation; Dynamics Statistical Method; Climatic Potential Productivity; Light-Temperature Potential Productivity

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K.O. Rauff, A.A. Ismail. Statistical Analysis Of Impact Of Climate Change On Crop Potentials Productivity On A Regional Scale In Nigeria. American Journal of Agricultural Research, 2019,4:40.

1. Ångström A.; (1924). Solar and atmospheric radiation. Quarterly J. of the Royal Meteor. Soc. 50: 121-126 (7 pages).
2. Barker H.W.; (1992). Solar radiative transfer through clouds possessing isotropic variable extinction coefficient. Quarterly J. of the Royal Meteor. Soc., 118(3):1145-1162 (17 pages)
3. Boisvert J.B.; Hayhoe H.N.; Dubé P.A.; (1990). Improving the estimation of global radiation across canada. Agricultural and Forest Meteor., 52(3-4): 275-286 (11 pages).
4. Boogaard H.L.; De Wit A.J.W.; te Roller J.A; Van Diepen C.A.; (2014). Wofost control centre 2.1; User’s guide for the wofost control centre 2.1 and the crop growth simulation model wofost 7.1.7. Wageningen (Netherlands), Alterra, Wageningen University & Research Centre (133 pages).
5. Chen C.; Baethgen W.E.; Robertson A.; (2013). Contributions of individual variation in temperature, solar radiation and precipitation to crop yield in the north china plain, 1961–2003. Climatic Change., 116(3-4): 767–788 (21 pages).
6. De Jong R.; Stewart D.W.; (1993). Estimating global radiation from common meteorological variables in Western Canada. Canadian J of Plant Sci., 73(2): 509-518 (9 pages).
7. Donatelli M.; Bellochi G.; Fontana F.; (2003). RadEst300: Software to estimate daily radiation data from commonly available meteorological variables. Eur. J. of Agronomy., 18(3-4): 363-367 (4).
8. Foken T.; (2008). Micrometeorology. Springer-Verlag Berlin Heidelberg., (306 pages).
9. Goudriaan J.; (1977). Crop micrometeorology: a simulation study. simulation monographs. pudoc, wageningen, Netherland. (262 pages).
10. Grassini P.; Yang H.; Cassman K.G.; (2009). Limits to maize productivity in western corn-belt: a simulation analysis for fully irrigated and rainfed conditions. Agri. Forest Meteor., 149(8): 1254–1265 (9 pages).
11. Keating B. A.; Carberry P.S.; Hammer G.L.; (2003). An overview of apsim, a model designed for farming systems simulation. Eur. J. of Agronomy., 18(3-4), 267–288 (21 pages).
12. Keyzer M.; Merbis M.; Pavel F.; (2002). Can we feed the animals? origins and implications of rising meat demand. Paper presented at 2002 International Congress, Eur. Assoc. of Agric. Econ., Zaragoza, Spain, 28–31 Aug.
13. National Aeronautics and Space Administration, Goddard Institute for Space Studies (NASA, GISS). 2015. NASA, NOAA Finds 2015 Warmest Year in Modern Record. 18 January. http://www.giss. (accessed 18 January 2017).
14. Penman H.L.; (1948). Natural evaporation from open water, bare soil and grass. Proceedings Royal Soc. Series A., 193(1032): 120-146 (26 pages).
15. Prescott J.A.; (1940). Evaporation from a water surface in relation to solar radiation.Transactions of the Royal Society of South Australia., 64: 114-480 (366 pages).
16. Supit I.; Hooijer A.A.; van Diepen C.A.; (1994). System description of the wofost 60 crop simulation model implemented in CGMS. Volume 1 theory and algorithms, Joint Research Centre Commission of the European Communities EUR 15956 EN: Luxembourg, 1994, (146 pages).
17. Wang T.; Lu C.; Yu B.; (2011). Production potential and yield gaps of summer maize in the Beijing-Tianjin-Hebei Region. J. Geograph. Sci., 21(4): 677–688 (11 pages).
18. Yang J.; Peter M. V.; Jian Y.; Michael E. G.; (2010). A Commentary on ‘Common SNPs Explain a Large Proportion of the Heritability for Human Height. Twin Research and Human Genetics., 13(6): 517-524 (8 pages)
19. Yuan Z.; Li J.; Zhang L.; Gao X.; Gao H.J.; Xu S.; (2012). Investigation on brca1 snps and its effects on mastitis in chinese commercial cattle. Elsevier., 505: 190-194 (5 pages)
20. Zheng-Hong Tan, Jiye Zeng, Yong-Jiang Zhang, Martijn Slot, Minoru Gamo, Takashi Hirano, Yoshiko Kosugi, Humberto R da Rocha, Scott R Saleska, Michael L Goulden (2017). Optimum air temperature for tropical forest photosynthesis: mechanisms involved and implications for climate warming2017 Environ. Res. Lett.12: 054022
21. Zunfu Lv, Xiaojun Liu, Weixing Cao and Yan Zhu (2017). A Model-Based Estimate of Regional Wheat Yield Gaps and Water Use Efficiency in Main Winter Wheat Production Regions of China. Scientific Reports volume 7, Article number: 6081