Climate Change and Food Security: The Impact of Some Key Variables on Wheat Yield in Kazakhstan
Abstract
:1. Introduction
2. Literature Review
3. Case Study
4. Methodology
5. Modelling Results
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Global Warming of 1.5 °C: An IPCC Special Report on the Impacts of Global Warming of 1.5 °C Above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty; WMO, UNEP: Geneva, Switzerland, 2014.
- United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development, Resolution Adopted by the General Assembly on 25 September 2015, A/RES/70/1; United Nations: New York, NY, USA, 2015. [Google Scholar]
- Porter, J.R.; Xie, L.; Challinor, A.J.; Cochrane, K.; Howden, S.M.; Iqbal, M.M.; Lobell, D.B.; Travasso, M.I. Food security and food production systems. In Climate Change 2014: Impacts, Adaptation, and Vulnerability—Part A: Global and Sectoral Aspects; Field, C.B.V.R., Barros, D.J., Dokken, K.J., Mach, M.D., Mastrandrea, T.E., Bilir, M., Chatterjee, K.L., Ebi, Y.O., Estrada, R.C., Genova, B., et al., Eds.; Cambridge University Press: Cambridge, UK, 2004; pp. 485–533. [Google Scholar]
- Ministry of Environment Protection. Kazakhstan’s Second National Communication to the Conference of the Parties to the United Nations Framework Convention on Climate Change; Ministry of Environment Protection: Astana, Kazakhstan, 2009.
- Vaughan, D.G.; Comiso, J.C.; Allison, I.; Carrasco, J.; Kaser, G.; Kwok, R.; Mote, P.; Murray, T.; Paul, F.; Ren, J.R.; et al. Observations: Cryosphere. In Climate Change 2013: The Physical Science Basis; Stocker, T.F.D., Qin, G.-K., Plattner, M., Tignor, S.K., Allen, J., Boschung, A., Nauels, Y., Xia, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK, 2013. [Google Scholar]
- Shahgedanova, M.; Nosenko, G.; Bushueva, I.; Ivanov, M. Changes in area and geodetic mass balance of small glaciers, Polar Urals, Russia, 1950–2008. J. Glaciol. 2012, 58, 953–964. [Google Scholar] [CrossRef] [Green Version]
- Food and Agriculture Organization (FAO). The State of Food and Agriculture: Climate Change, Agriculture and Food Security; Food and Agriculture Organization of the United Nations: Rome, Italy, 2016. [Google Scholar]
- Guo, H.; Bao, A.; Liu, T.; Jiapaer, G.; Ndayisaba, F.; Jiang, L.; Kurban, A.; De Maeyer, P. Spatial and temporal characteristics of droughts in Central Asia during 1966–2015. Sci. Total Environ. 2018, 624, 1523–1538. [Google Scholar] [CrossRef] [PubMed]
- Portela, M.M.; Zeleňáková, M.; Silva, A.T.; Hlavatá, H.; Santos, J.F.; Purcz, P. Comprehensive Characterization of Droughts in Slovakia. Int. J. Environ. Sci. Dev. 2017, 8, 25–29. [Google Scholar] [CrossRef] [Green Version]
- Gelcer, E.; Fraisse, C.; Dzotsi, K.; Hu, Z.; Mendes, R.; Zotarelli, L. Effects of El Niño Southern Oscillation on the space–time variability of Agricultural Reference Index for Drought in midlatitudes. Agric. For. Meteorol. 2013, 174–175, 110–128. [Google Scholar] [CrossRef]
- Elagib, N. Development and application of a drought risk index for food crop yield in Eastern Sahel. Ecol. Indic. 2014, 43, 114–125. [Google Scholar] [CrossRef]
- Ubilava, D.; Holt, M. El Niño southern oscillation and its effects on world vegetable oil prices: Assessing asymmetries using smooth transition models. Aust. J. Agric. Resour. Econ. 2013, 57, 273–297. [Google Scholar] [CrossRef] [Green Version]
- Iizumi, T.; Luo, J.-J.; Challinor, A.J.; Sakurai, G.; Yokozawa, M.; Sakuma, H.; Brown, M.E.; Yamagata, T. Impacts of El Niño Southern Oscillation on the Global Yields of Major Crops. Nat. Commun. IPCC 2018, 5, 3712. [Google Scholar] [CrossRef] [Green Version]
- Ward, P.J.; Jongman, B.; Kummu, M.; Dettinger, M.D.; Weiland, F.C.S.; Winsemius, H.C. Strong influence of El Nino Southern Oscillation on flood risk around the world. Proc. Natl. Acad. Sci. USA 2014, 111, 15659–15664. [Google Scholar] [CrossRef] [Green Version]
- Svensmark, H. Cosmic Rays and Earth’s Climate. Space Sci. Rev. 2000, 93, 175–185. [Google Scholar] [CrossRef]
- Svensmark, H. Cosmic Rays, Clouds and Climate. Europhys. News 2015, 46, 26–29. [Google Scholar] [CrossRef]
- Iglesias, A.; Erda, L.; Rosenzweig, C. Climate Change in Asia: A Review of the Vulnerability and Adaptation of Crop Production; Springer: Dordrechti, The Netherlands, 1996; pp. 13–27. [Google Scholar]
- Min, M.; Zhao, W.; Hu, T.; Chen, J.; Nie, X. Influential Factors of Spatial Distribution of Wheat Yield in China During 1978–2007: A Spatial Econometric Analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 2014, 7, 4453–4460. [Google Scholar] [CrossRef]
- Liu, Y.; Yang, X.; Wang, E.; Xue, C. Climate and crop yields impacted by ENSO episodes on the North China Plain: 1956–2006. Reg. Environ. Chang. 2013, 14, 49–59. [Google Scholar] [CrossRef] [Green Version]
- Xu, K.; Dawen, Y.; Hanbo, Y.; Zhe, L.; Yue, Q.; Yan, S. Spatio-Temporal Variation of Drought in China during 1961–2012: A Climatic Perspective. J. Hydrol. 2015, 526, 253–264. [Google Scholar] [CrossRef]
- Subash, N.; Gangwar, B. Statistical analysis of Indian rainfall and rice productivity anomalies over the last decades. Int. J. Clim. 2013, 34, 2378–2392. [Google Scholar] [CrossRef]
- Selvaraju, R. Impact of El Niño-southern oscillation on Indian foodgrain production. Int. J. Clim. 2003, 23, 187–206. [Google Scholar] [CrossRef]
- Ahmed, M.; Hassan, F.-U. Cumulative Effect of Temperature and Solar Radiation on Wheat Yield. Not. Bot. Horti Agrobot. Cluj-Napoca 2011, 39, 146–152. [Google Scholar] [CrossRef] [Green Version]
- Ahmad, M.; Siftan, H.; Iqbal, M. Impact of Climate Change on Wheat Productivity in Pakistan: A District Level Analysis. MPRA Pap. 2016. [Google Scholar] [CrossRef]
- Huang, J.; Ma, J.; Guan, X.; Li, Y.; He, Y. Progress in Semi-arid Climate Change Studies in China. Adv. Atmos. Sci. 2019, 36, 922–937. [Google Scholar] [CrossRef]
- Garnett, E.R.; Khandekar, M.L.; Babb, J.C. On the Utility of ENSO and PNA Indices for Long-Lead Forecasting of Summer Weather over the Crop-Growing Region of the Canadian Prairies. Theor. Appl. Clim. 1998, 60, 37–45. [Google Scholar] [CrossRef]
- Hansen, J.W.; Jones, J.W.; Kiker, J.C.; Hodges, A.W. El Niño–Southern Oscillation Impacts on Winter Vegetable Production in Florida. J. Clim. 1999, 12, 92–102. [Google Scholar] [CrossRef]
- Soler, C.M.T.; Sentelhas, P.C.; Hoogenboom, G. The impact of El Niño Southern Oscillation phases on off-season maize yield for a subtropical region of Brazil. Int. J. Clim. 2009, 30, 1056–1066. [Google Scholar] [CrossRef]
- Beltrán-Przekurat, A.; Sr, R.A.P.; Eastman, J.L.; Coughenour, M.B. Modelling the effects of land-use/land-cover changes on the near-surface atmosphere in southern South America. Int. J. Clim. 2011, 32, 1206–1225. [Google Scholar] [CrossRef] [Green Version]
- Okonkwo, C.; Demoz, B. The relationship between El Niño Southern Oscillations and cereal production in Sahel. Environ. Hazards 2014, 13, 343–357. [Google Scholar] [CrossRef]
- Plisnier, P.D.; Serneels, S.; Lambin, E.F. Impact of ENSO on East African ecosystems: A multivariate analysis based on climate and remote sensing data. Glob. Ecol. Biogeogr. 2000, 9, 481–497. [Google Scholar] [CrossRef]
- Kanno, H.; Sakurai, T.; Shinjo, H.; Miyazaki, H.; Ishimoto, Y.; Saeki, T.; Umetsu, C. Analysis of Meteorological Measurements Made over Three Rainy Seasons and Rainfall Simulations in Sinazongwe District, Southern Province, Zambia. Jpn. Agric. Res. Q. 2015, 49, 59–71. [Google Scholar] [CrossRef] [Green Version]
- Gimeno, L.; Ribera, P.; Iglesias, R.; De La Torre, L.; García, R.; Hernández, E. Identification of empirical relationships between indices of ENSO and NAO and agricultural yields in Spain. Clim. Res. 2002, 21, 165–172. [Google Scholar] [CrossRef] [Green Version]
- Frías, M.D.; Herrera, S.; Cofiño, A.S.; Gutiérrez, J.M.; García, S.H. Assessing the Skill of Precipitation and Temperature Seasonal Forecasts in Spain: Windows of Opportunity Related to ENSO Events. J. Clim. 2010, 23, 209–220. [Google Scholar] [CrossRef]
- Wallace, J.M.; Lim, G.-H.; Blackmon, M.L. Relationship between Cyclone Tracks, Anticyclone Tracks and Baroclinic Waveguides. J. Atmos. Sci. 1988, 45, 439–462. [Google Scholar] [CrossRef] [Green Version]
- Rasmusson, E.M.; Hall, J.M. El Niño: The Great Equatorial Warming: Pacific Ocean Event of 1982–1983. Weatherwise 1983, 36, 166–176. [Google Scholar] [CrossRef]
- Fraedrich, K. European grosswetter during the warm and cold extremes of the El Niñco/Southern Oscillation. Int. J. Clim. 1990, 10, 21–31. [Google Scholar] [CrossRef]
- Volkov, Y.N.; Kalashnikov, B.M. El Niño: Identification and Possibility of Forecasting. Work. Fehmri 1990, 158–172. [Google Scholar]
- Gruza, G.V.; Ranjkova, E.; Kleshchenko, L.K.; Aristova, L.N. On Relations of Climatic Abnormalities on the Territory of Russia with El Nino Event—South Oscillation. Meteorol. Hydrol. 2002, 5, 32–51. [Google Scholar]
- Petrosyants, M.A.; Gushchina, D.Y. On Definition of El Niño and La Niña Events. Meteorol. Hydrol. 2002, 8, 24–35. [Google Scholar]
- Perevedentsev, Y.P.; Shattalinskiy, K.M.; Vazhnova, N.A.; Naumov, E.P.; Shumikhina, A.V. Climate Changes on the Territory of Privolzhsky Federal District for the Last Decades and Its Connection with the Geophysical Factors. Bull. Udmurt Univ. 2012, 4, 122–135. [Google Scholar]
- Zarch, A.; Amin, M.; Sivakumar, B.; Sharma, A. Droughts in a Warming Climate: A Global Assessment of Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI). J. Hydrol. 2015, 526, 183–195. [Google Scholar] [CrossRef]
- Mokhov, I.I.; Smirnov, D.A. El Niño–Southern Oscillation drives North Atlantic Oscillation as revealed with nonlinear techniques from climatic indices. Geophys. Res. Lett. 2006, 33. [Google Scholar] [CrossRef] [Green Version]
- Weare, B.C.; Mokhov, I.I. Evaluation of Total Cloudiness and Its Variability in the Atmospheric Model Intercomparison Project. J. Clim. 1995, 8, 2224–2238. [Google Scholar] [CrossRef] [Green Version]
- Polonsky, A.B.; Voskresenskaya, E.N. On the Statistical Structure of Hydrometeorological Fields in the North Atlantic. Phys. Oceanogr. 2004, 14, 15–26. [Google Scholar] [CrossRef]
- Kryjov, V.N.; Park, C.-K. Solar modulation of the El-Nino/Southern oscillation impact on the Northern Hemisphere annular mode. Geophys. Res. Lett. 2007, 34, L10701. [Google Scholar] [CrossRef]
- Nesterov, E.S. The North Atlantic Oscillation: The Atmosphere and the Ocean; Triada: Houston, TX, USA, 2013; p. 144. [Google Scholar]
- Government of Kazakhstan. Program on Desertification Control in the Republic of Kazakhstan for 2005–2015 Period; Government of the Republic of Kazakhstan: Nur-Sultan, Kazakhstan, 2005. [Google Scholar]
- Sidorenkov, N.S. Celestial mechanical causes of weather and climate change. Izv. Atmos. Ocean. Phys. 2016, 52, 667–682. [Google Scholar] [CrossRef]
- Yulihastin, E.; Nur, F. Trismidianto: Impacts of El Niño and IOD on the Indonesian Climate; National Institute of Aeronautics and Space: Jakarta, Indonesia, 2008.
- Diamond, H.J.; Lorrey, A.M.; Renwick, J.A. A Southwest Pacific Tropical Cyclone Climatology and Linkages to the El Niño–Southern Oscillation. J. Clim. 2013, 26, 3–25. [Google Scholar] [CrossRef]
- Zhao, C.; Liu, B.; Xiao, L.; Hoogenboom, G.; Boote, K.J.; Kassie, B.T.; Pavan, W.; Sheila, V.; Kim, K.S.; Hernandez-Ochoa, I.; et al. A SIMPLE Crop Model. Eur. J. Agron. 2019, 104, 97–106. [Google Scholar] [CrossRef]
- Makaudze, E.M. Assessing the economic value of El Niño-based seasonal climate forecasts for smallholder farmers in Zimbabwe. Meteorol. Appl. 2012, 21, 535–544. [Google Scholar] [CrossRef]
- WMO. National Drought Management Policy Guidelines: A Template for Action; World Meteorological Organization: Geneva, Switzerland, 2014. [Google Scholar]
- WMO. Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2012); World Meteorological Organization: Geneva, Switzerland, 2014. [Google Scholar]
- Palmer, W.C. Meteorological Drought, Research Paper No. 45, Washington, D.C.: U.S. Department of Commerce & Weather Bureau. 1965. Available online: https://www.ncdc.noaa.gov/temp-and-precip/drought/docs/palmer.pdf (accessed on 28 June 2021).
- Gringof, I.G.; Paseciniuc, A.D. Agrometeorology and Agro-Meteorological Observations; Gidrometeoizdat: Leningrad, Russia, 2005. [Google Scholar]
- Zhumbaev, E.E. Strategic Measures Combating Desertification in the Republic of Kazakhstan until 2025; UNDP: Nur-Sultan, Kazakhstan, 2015. [Google Scholar]
- Ormes, J.F. Cosmic Rays and Climate. Adv. Space Res. 2018, 62, 2880–2891. [Google Scholar] [CrossRef]
- Wolter, K. The Southern Oscillation in Surface Circulation and Climate over the Tropical Atlantic, Eastern Pacific, and Indian Oceans as Captured by Cluster Analysis. J. Clim. Appl. Meteorol. 1987, 26, 540–558. [Google Scholar] [CrossRef] [Green Version]
- Wolter, K.; Timlin, M.S. El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext). Int. J. Clim. 2011, 31, 1074–1087. [Google Scholar] [CrossRef]
- Marsh, N.; Svensmark, H. Galactic cosmic ray and El Niño–Southern Oscillation trends in International Satellite Cloud Climatology Project D2 low-cloud properties. J. Geophys. Res. Space Phys. 2003, 108. [Google Scholar] [CrossRef]
- Shakhov, A.A. The Effect of Cosmic Radiation on Plant Activity. Zhurnal Obs. Biol. 1962, 23, 81–89. [Google Scholar]
- Dengel, S.; Aeby, D.; Grace, J. A relationship between galactic cosmic radiation and tree rings. New Phytol. 2009, 184, 545–551. [Google Scholar] [CrossRef]
№ | Author, Year | Region and Country | Research Focus | Method | Variables | Conclusions |
---|---|---|---|---|---|---|
1. | Iizumi et al., 2014 [13] | Global: USA, China, Kazakhstan, Mexico, Tanzania, Australia, India, etc. | Maize, soybean, rice, wheat | Statistical analysis: polynomial regression | Sea surface temperature (SST) anomaly, Oceanic Niño Index, La Niña, surface air temperature, and soil moisture | El Niño results in negative impacts on yields in 22–24% of harvested areas worldwide: maize in USA, China, Mexico, and Indonesia; soybean in India and China; rice in China, Myanmar, and Tanzania; and wheat in China, USA, Australia, Mexico, and parts of Europe. In contrast, El Niño impacts positively on 30–36% of harvested areas worldwide: wheat in Argentina, Kazakhstan, and South Africa; rice in China, Indonesia, and Brazil; soybean in the USA and Brazil; and maize in Brazil and Argentina. |
2. | Ward et al., 2014 [14] | Global except Antarctica and Greenland | Flood risk | Cascade of hydrological and hydraulic models; statistical analyses: Spearman’s rank | Daily gridded discharge and flood volume at a horizontal resolution of 0.5° ×0.5° forced by daily meteorological fields; El Niño and La Niña | Flood anomalies revealed during El Niño and La Niña years: 34% of Earth surface excluding Antarctica and Greenland in El Niño years and 38% in La Niña years, respectively. At the regional scale strong flood anomalies are observed in the Sahel, western and southern Africa, Australia, the western United States (esp. in La Niña years), South America, and central Eurasia (especially during El Niño years). |
3. | Svensmark, 2000 [15] | Global | Cosmic rays and Earth’s climate | Analytical review | Cosmic ray flux, solar cycle, Earth’s climate; clouds; Earth’s temperature; volcanic dust; El Niño–Southern Oscillation | During the last solar cycle, changes in the Earth’s cloud cover are correlated with the Galactic Cosmic Ray variations. It was found that on the time horizon over 10 years the sun has a significant influence on climate variations based on correlation between isotopes and Earth’s temperature for the last 1000 years assessed by proxy data. |
4. | Ormes, 2018 [48] | Global | Cosmic rays and climate | Analytical review | Solar irradiance and climate; The Little Ice Age; cosmic ray ion intensity; direct effects of cosmic rays on climate: the Svensmark Effect; cloud classification | Galactic Cosmic Ray ionization effect in the troposphere could amount to 5–10%, whereas at latitudes above 50, this could be as high as 15–20%, affecting cloud formation through stimulating cloud condensation nuclei. |
5. | Sidorenkov, 2016 [49] | Global | Synchronization of atmospheric processes with frequencies of the Earth–moon–sun system | Analytical review; comparative analysis | The movement of the poles of the Earth; quasi-two-year cyclic (QTC) zonal wind; the Chandler movement of the Earth’s poles (CMP); Multanovsky’s Natural synoptic periods (NSP); decade climate change | The author demonstrates how the 35-year Brückner cycle is correlated with the beat of the annual solar (365 days) and annual lunar (355 days) changes in meteorological parameters. A hypothesis is proposed on how the lunisolar tides influence the air temperature via the radiation conditions in the atmosphere due to fluctuations in the cloud cover. The author concludes that climatic characteristics, temperature, monsoons, masses of ice sheets, etc., correlate with changes in the speed of rotation of the Earth. |
6. | Guo et al., 2018 [8] | Central Asia | Spatial and temporal characteristics of droughts in central Asia during 1966–2015 | Principle Components Analysis (PCA); varimax rotation method; the Sen’s slope and the Modified Mann–Kendall method (MMK); continuous wavelet analysis | Climatic indices: North Atlantic Oscillation, Siberian High Index, Tibetan Plateau Index; PCA regionalization; Standardized Precipitation Evapotranspiration Index; drought | The drought patterns differ substantially in central Asia with most significant droughts observed in 1973–1979 (NW, NK, SW, and SE); 1983–1988 (SE and HX), and 1997–2003 (NE, NK, SW, SE, and HX) and an overall wetting tendency between 1966 to 2015. Most regions except north Kazakhstan exhibit a drying trend in 2003–2015. ENSO has an influence on drought variation over the whole region of central Asia; NAO has significant impact everywhere except HX. NAO and SPEI demonstrate short-period strongly significant correlations in northern Kazakhstan for 1974, 1983, 1992, 1996, 2005, and 2011. |
7. | Min et al., 2014 [18] | China | Wheat yield | Exploratory spatial data analysis (ESDA): spatial autocorrelation and spatial heterogeneous dynamic analysis of wheat yield; Durbin model | Wheat yield; the average monthly precipitation; the average monthly temperature during; input of fertilizer; input of seed; effective irrigation area of wheat; disaster area of wheat | Wheat productivity in China in 1978–2007 is both affected by climate change and adaptive management. Temperature does not have a significant impact on wheat yield and precipitation has a significant negative effect. Wheat yield is significantly influenced by irrigation and damaged area during its growth. The authors infer that the adaptive management has played a more important role than climate change for wheat yield. |
8. | Liu et al., 2014 | [19] China | Wheat and maize | Statistical analysis: linear regression, Student’s t-test, Kolmogorov–Smirnov (KS) test. Agricultural production systems simulator (APSIM) | Daily weather data, data on yields of winter wheat and summer maize, SST over the tropical Pacific (ENSO index) | Crop yields on the North China Plain were affected by ENSO phases. The wheat yield probability distributions were exceptionally high for El Niño and La Niña years, although in Nanyang region the differences between El Niño and La Niña events were significant only at the 5% level. For maize the lowest probability distributions were seen in El Niño years and reached the confidence level of 95% in La Niña years. |
9. | Xu et al., 2015 [20] | China | Climate variability and trends at a national scale | Analytical review; regression model; China Meteorological Data Sharing System; fractal dimensions and trend indices of each climatic factor for 1960–2013 in the 579 meteorological stations; Mann–Kendall tests for trends; Hurst index of meteorological parameters; mapping by ArcGIS10 software | 3 months Standardized Precipitation Index (SPI3), 3 months Reconnaissance Drought Index (RDI3), 3 month Standardized Precipitation Evapotranspiration Index (SPEI3) | High spatial resolution daily climate variability data from 1960 to 2013 were used in conjunction with a 3 months Standardized Precipitation Index (SPI3), 3 months Reconnaissance Drought Index (RDI3), and a 3 months Standardized Precipitation Evapotranspiration Index (SPEI3). The most severe droughts of 1962–1963 and 2010–2011 in China affected over half of the nonarid regions. These droughts were the strongest in regions from the North China Plain to the downstream of the Yangtze River. SPI and RDI were found to be more appropriate than SPEI in the arid regions. |
10. | Subash and Gangwar, 2014 [21] | India | Kharif rice yield | Interannual variability, standard deviation, coefficient of variation, Mann–Kendall nonparametric test | Monthly rainfall data, production and productivity of kharif rice, Oceanic Niño Index (ONI) | Rice productivity was found to be related to monsoon fluctuations. Six out of eight El Niño years exhibited a reduction in rainfall during the monsoon season, ranging from −20.3% in 2002 to −5.5% in 1991. A higher deficit was observed in July and September. During 5 out of 8 moderate and strong El Niño years the kharif rice productivity reduced −4.3% in 1986 and −13.8% in 2002. There exists a wide spatial variability in rice productivity. July rainfall influenced 71% of the variations in rice productivity. |
11. | Selvaraju, 2003 [22] | India | Rice, sorghum, maize, pigeon pea, blackgram wheat, chickpea | Hamming-type filter, three-point weighted moving average using Hanning weights, correlation coefficients, Kruskal–Wallis (KW) H-test | Time series of actual total food grain production, NINO3 SST index, averaged SST anomalies, summer monsoon rainfall (SMR) | Crop yield anomalies in India were found to be inversely and statistically significantly related to SST anomalies from June to August over TPO NINO3 sector. Rice tends to be more affected than wheat. Total yields decreased by 1–15% during warm ENSO phase. Economic impacts were found to be significant, reducing revenue by up to USD 2.2 billion during a warm ENSO period and increasing revenues by USD 1.3 billion in a cold ENSO period. NINO3 ENSO index was concluded to be a good policy predictor for agriculture in India. |
12. | Iglesias et al., 1996 [17] | India, Bangladesh, Pakistan, Japan, south China | Soybeans, maize, wheat, roots, tubers, fruit, vegetables | General circulation models (GCMs); climate scenarios; IBSNAT crop models | Temperature and precipitation, doubled CO2 | The estimated effects of climate change on crop yields in Asia measure up to +/- 20%. South and southeast Asian countries were found to be more affected; east Asia was considered to be less vulnerable. Fluctuations in ENSO event frequency and severity were shown to be having an impact on agricultural production in Asia. |
13. | Ahmed and Hassan, 2011 [23] | Pakistan | Wheat yield | Statistical analysis; scatter plot regression model | Crop data and climate variables (T1, T2, SR1, SR2, PTQ1, and PTQ2) | Yields were found to be statistically related to solar radiation and temperature. |
14. | Ahmad et al., 2014 [24] | Pakistan | Wheat yield | Regression model | Solar radiation, crop yields | Climatic change is having an impact on wheat yields in Pakistan. An increase of 1 °C in the mean temperature during the germination and tillering stages is capable of reducing yields by 7.4%. Conversely, a 1 °C increase in the mean temperature during the vegetative growth stage improves productivity by 6.4%. |
15. | Yulihastin et al., 2008 [50] | The Indonesian Maritime Continent (IMC) | Rainfall | Dipole mode index | El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) mode, SST | ENSO and IOD tend to influence rainfall in Indonesia. During a combined strong El Niño phase and a strong positive IOD, Indonesia experiences negative anomalies of precipitations during June–August, September–November, and December–February. |
16. | Diamond et al., 2012 [51] | Southwest Pacific | Southwest Pacific tropical cyclone | 20th-century reanalysis data and the coupled ENSO index (CEI) | Mean sea level pressure difference between Papeete, French Polynesia and Darwin, Australia; El Niño and La Niña atmospheric circulation | Positive correlations were found between Tropical Cyclones (TCs), sea surface temperature, and atmospheric circulation. The period of 1991–2010 exhibited higher TC frequency as opposed to the 1970–1990 period. TCs were found to be connected to sea surface temperature (SSTs) in the SW Pacific basin and atmospheric circulation in specific regions. |
17. | Huang et al., 2016 [25] | Japan, Australia, and China | Rice yield | Analytical review; linear regression; box diagrams | Rice; grain yield; biomass production; harvest index; radiation use efficiency (RUE); solar radiation; intercepted radiation | Up to 73% and 6% of the yield variation was explained by biomass production and the harvest index, respectively. Biomass production was strongly positively correlated to intercepted radiation. |
18. | Garnett et al., 1998 [26] | Canada | Summer weather forecasting | Multiple regression analysis; linear correlation coefficients | PNA and ENSO indices, summer mean temperature, and mean total precipitation | Monthly values of PNA and ENSO indices could predict summer weather in Canadian prairies 2–7 months in advance, which could be beneficial for agriculture. |
19. | Zhao et al., 2019 [52] | USA | Cereals, roots, vegetables, and fruits | SIMPLE crop model: model calibration, sensitivity analysis; simulation | 13 parameters; climate impact | The SIMPLE crop model includes 13 parameters including crop type, daily weather data, crop management, and soil water holding parameters, and was calibrated for 14 crops. The limitations of the model include lack of response to vernalization and photoperiod effect on phenology, and the model doesn’t include nutrient dynamics. |
20. | Ubilava and Holt, 2013 [12] | USA | Corn production | Smooth Transition Vector Error Correction Models (STVECM); Smooth Transition Autoregressive Models (STAR) | Panel of crop yield, temperature, precipitation, and ENSO data (anomaly, Niño 3.4) | ENSO dynamics were found to include an autoregressive nonlinearity. Palm oil, soybean oil, sunflower seed oil, and rapeseed oil are significantly affected by climate anomalies driven by ENSO events. |
21. | Hansen et al., 1999 [27] | Southeastern USA | Peanut, tomato, cotton, tobacco, corn, and soybean | Canonical correlation analysis; Fourier analysis; spectral analysis, analysis of variance (ANOVA) | Historical records of yields, area harvested, price, and total value of production, sea surface temperature anomalies | Crop yields were shown to be higher in La Niña phases and lower than the trend in years immediately following La Niña phases. Corn and tobacco yields were found to be statistically significantly affected by ENSO phases. Bell pepper prices exhibited a correlation with ENSO events, which didn’t apply to tomatoes. |
22. | Soler et al., 2010 [28] | Brazil | Maize yield | CSM-CERES-Maize model, DSSAT model | El Niño, La Niño daily weather data; soil characterization data; cultivar coefficients; crop management information | A higher probability of increased rainfall in April and May in Sao Paulo during an El Niño phase was found. The study explored the significance of ENSO phases on planting dates and yields. |
23. | Beltrán- Przekurat et al., 2012 [29] | Argentina | Land-use and land-cover change | Coupled atmospheric- biospheric regional climate GEMRAMS model | The near-surface energy balance, temperature, humidity, and precipitation | Land-use/land-cover changes (LULCC) were found to influence the albedo, near-surface energy balance, humidity, precipitation, and temperature over southern South America. |
24. | Okonkwo and Demoz, 2014 [30] | Sahel (Africa) | Maize, millet, and sorghum | Wavelet analysis (CWT, XWT), correlation analysis | Niño 3.4, SST, precipitation, temperature, soil moisture | Statistically significant correlation between ENSO and crop yield in the Sahel region was confirmed. El Niño events appear to have a negative impact on millet yield and a positive impact on maize and sorghum production. The indirect impact of ENSO on crop yield is through its effect on temperature and available rainfall during the short planting period in Sahel. |
25. | Plisnier et al., 2000 [31] | East Africa | East African ecosystems, Lake Tanganyika | Statistical analysis: moving average (MA) filter | SOI, rainfall, air humidity, NDVI, Ts | A positive and significant correlation between the air temperature in east Africa and Pacific SST anomalies was found. Warm ENSO events tend to coincide with drier conditions. Correlation coefficients between NDVI and Ts anomalies and the ENSO index were found to be highly significant. |
26. | Makaudze, 2014 [53] | Southern Africa, Zimbabwe | Maize yield | The Decision Support System for Agricultural Technology program, DSSAT v4 | Crop inputs, daily weather data: solar radiation, rainfall, temperature evapotranspiration, El Niño, La Niño | Climate forecasts have shown to lead to smallholder farmers in Zimbabwe recording higher yield gains (28%) compared to those without forecasts. |
27. | Kanno et al., 2015 [32] | Zambia | Rainy seasons and rainfall simulations | Weather Research and Forecasting Model (WRF) ver. 3.2 | Air temperature, relative humidity, precipitation, wind direction and speed | The three rainy seasons in Zambia corresponded in turn with La Niña, normal, and El Niño conditions. The intra-seasonal variations in precipitation appeared to be driven by ENSO. Variations in temperature, wind, and humidity over time appeared to be highly sensitive to large-scale atmospheric movements, such as ENSO. |
28. | Gimeno et al., 2002 [33] | Spain | Wheat, rye, barley, oats, sunflower seeds, olives, grapes, citrus fruits | ANOVA analysis; cross-correlation functions (CCF) | ENSO and NAO phases; crop yields | Orange and tangerine yields were found to be highly correlated to fluctuations in ENSO. Wheat, orange, and lemon showed correlation with the NAO index. Wheat, sunflower, barley, rye, olive, grape, and tangerine tend to exhibit lower yields during La Niña phases than during El Niño phases. At the same time, oats, rye, wheat, and citrus yields are higher in positive NAO phases in Spain. |
29. | Frias et al., 2010 [34] | Spain | Precipitation and temperature seasonal forecasts | Multimodel DEMETER system; statistical downscaling method | Seven global atmosphere–ocean coupled models, each running from an ensemble of nine initial conditions, ENSO events | ENSO is found to be affecting dry events in spring in the south and Mediterranean coast and hot events in the southern part of Spain during El Niño years. La Niña events manifest themselves in dry events in winter in the northwest of Spain and hot events in the summer in the south and Mediterranean coast. |
Variables | Coefficient | Std. Error | t-Value | t-Prob | Part.R2 |
---|---|---|---|---|---|
Change in YIELD | 0.484347 | 0.07903 | 6.13 | 0.0000 | 0.5643 |
Change in Temperature [June] | −0.255337 | 0.1347 | −1.90 | 0.0680 | 0.1103 |
Change in Total Solar Irradiancet−1 | −6.48107 | 1.893 | −3.42 | 0.0019 | 0.2880 |
Change in Wolft−1 | 0.0465606 | 0.01537 | 3.03 | 0.0051 | 0.2403 |
Cosmic Rays [December] t−1 | 0.00117928 | 2.894 × 10−5 | 40.7 | 0.0000 | 0.9828 |
Soil Moisture | 1.18548 | 0.2816 | 4.21 | 0.0002 | 0.3792 |
MEI [June] t−1 | −1.20924 | 0.3088 | −3.92 | 0.0005 | 0.3459 |
ECM t−1 | 0.775867 | 0.09002 | 8.62 | 0.0000 | 0.7192 |
Variables | Coefficient | Std. Error | t-Value | t-Prob | Part.R2 |
---|---|---|---|---|---|
Precipitation [June] | 0.00348383 | 0.0006696 | 5.20 | 0.0000 | 0.4915 |
Precipitation [July] | 0.00358584 | 0.0006185 | 5.80 | 0.0000 | 0.5456 |
Precipitation [August] | 0.00416834 | 0.0008602 | 4.85 | 0.0000 | 0.4561 |
Precipitation [October] | 0.00316804 | 0.001026 | 3.09 | 0.0045 | 0.2539 |
Temperature [July] | −0.118263 | 0.01035 | −11.4 | 0.0000 | 0.8233 |
I1987 | 1.45623 | 0.3843 | 3.79 | 0.0007 | 0.3389 |
I1991 | −0.998864 | 0.3810 | −2.62 | 0.0140 | 0.1971 |
I1993 | 1.18435 | 0.3848 | 3.08 | 0.0046 | 0.2528 |
I1994 | 1.22896 | 0.3822 | 3.22 | 0.0033 | 0.2697 |
I2005 | −1.03599 | 0.3944 | −2.63 | 0.0138 | 0.1977 |
Variables | Coefficient | Std. Error | t-Value | t-Prob |
---|---|---|---|---|
Precipitation [June] | 0.00334953 | 0.0006376 | 5.25 | 0.0000 |
Precipitation [July] | 0.00402316 | 0.0005775 | 6.97 | 0.0000 |
Precipitation [August] | 0.00378489 | 0.0007966 | 4.75 | 0.0001 |
Precipitation [October] | 0.00278728 | 0.0009193 | 3.03 | 0.0069 |
Temperature [July] | −0.117935 | 0.01046 | −11.3 | 0.0000 |
I1987 | 1.37304 | 0.3439 | 3.99 | 0.0008 |
I1991 | −0.750801 | 0.3380 | −2.22 | 0.0387 |
I1993 | 1.23502 | 0.3581 | 3.45 | 0.0027 |
I1994 | 1.37440 | 0.3549 | 3.87 | 0.0010 |
I2005 | −0.851416 | 0.3532 | −2.41 | 0.0262 |
Variables | Coefficient | Std. Error | t-Value | t-Prob |
---|---|---|---|---|
Change in YIELD | 0.512376 | 0.07265 | 7.05 | 0.0000 |
Change in Temperature [June] | −0.307918 | 0.1263 | −2.44 | 0.0247 |
Change in Total Solar Irradiancet−1 | −6.50333 | 1.767 | −3.68 | 0.0016 |
Change in Wolft−1 | 0.0508515 | 0.01450 | 3.51 | 0.0024 |
Cosmic Rays [December] t−1 | 0.00118554 | 3.080 × 10−5 | 38.5 | 0.0000 |
MEI [June] t−1 | −1.29832 | 0.2851 | −4.55 | 0.0002 |
Soil Moisture | 1.32344 | 0.2979 | 4.44 | 0.0003 |
ECM t−1 | 0.834856 | 0.08633 | 9.67 | 0.0000 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Shmelev, S.E.; Salnikov, V.; Turulina, G.; Polyakova, S.; Tazhibayeva, T.; Schnitzler, T.; Shmeleva, I.A. Climate Change and Food Security: The Impact of Some Key Variables on Wheat Yield in Kazakhstan. Sustainability 2021, 13, 8583. https://doi.org/10.3390/su13158583
Shmelev SE, Salnikov V, Turulina G, Polyakova S, Tazhibayeva T, Schnitzler T, Shmeleva IA. Climate Change and Food Security: The Impact of Some Key Variables on Wheat Yield in Kazakhstan. Sustainability. 2021; 13(15):8583. https://doi.org/10.3390/su13158583
Chicago/Turabian StyleShmelev, Stanislav E., Vitaliy Salnikov, Galina Turulina, Svetlana Polyakova, Tamara Tazhibayeva, Tobias Schnitzler, and Irina A. Shmeleva. 2021. "Climate Change and Food Security: The Impact of Some Key Variables on Wheat Yield in Kazakhstan" Sustainability 13, no. 15: 8583. https://doi.org/10.3390/su13158583