Review Articles
Vol. 13 No. sp1 (2026): Recent Advances in Agriculture
Forecasting crop yields under climate oscillations: Implications for agricultural planning and resilience
Department of Agro-Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India
Department of Agro-Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India
Abstract
Climate oscillations such as the El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and North Atlantic Oscillation (NAO) significantly influence global weather variability, posing challenges to agricultural productivity and food security. Their impacts−ranging from altered rainfall patterns to temperature extremes−disrupt crop growth, especially in rainfed systems. Understanding these oscillations is vital for enhancing yield prediction and informing adaptive agricultural planning. This review synthesizes mechanistic insights and empirical findings from peer-reviewed literature on the influence of ENSO, IOD and NAO on crop yields across major agro-climatic zones. It also evaluates predictive tools, including statistical models, dynamic crop simulations and AI-driven forecasting systems. Crop-specific vulnerabilities and regional disparities in oscillation impacts were systematically analyzed to assess adaptation needs. Findings reveal that ENSO, IOD and NAO generate region-specific yield anomalies by modulating soil moisture, evapotranspiration and phenological development. Crops such as rice, maize and wheat exhibit heightened sensitivity during key growth stages under oscillation-driven stressors. Modern forecasting models incorporating oscillation indices improve predictive accuracy and provide early warnings for yield variability. However, gaps remain in translating forecasts into actionable farm-level decisions, especially in resource-limited regions. To build agricultural resilience, integrating oscillation-based forecasts into local advisory services, promoting climate-smart practices and adopting inclusive, region-specific adaptation strategies are essential. Bridging science-policy gaps and strengthening climate services will support anticipatory planning and safeguard food systems under increasing climate variability.
References
- 1. Ray DK, Gerber JS, MacDonald GK, West PC. Climate variation explains a third of global crop yield variability. Nat Commun. 2015;6(1):5989. https://doi.org/10.1038/ncomms6989
- 2. Coumou D, Rahmstorf S. A decade of weather extremes. Nat Clim Change. 2012;2(7):491-6. https://doi.org/10.1038/nclimate1452
- 3. Porter J, Xie L, Challinor A, Cochrane K, Howden S, Iqbal M, et al. Food security and food production systems climate change 2014: impacts, adaptation and vulnerability. Part a: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge (UK): Cambridge University Press; 2014. p. 485-533.
- 4. Saji N, Goswami BN, Vinayachandran P, Yamagata T. A dipole mode in the tropical Indian Ocean. Nature. 1999;401(6751):360-3. https://doi.org/10.1038/43854
- 5. Ummenhofer CC, England MH, McIntosh PC, Meyers GA, Pook MJ, Risbey JS, et al. What causes southeast Australia's worst droughts? Geophys Res Lett. 2009;36(4). https://doi.org/10.1029/2008GL036801
- 6. Cai W, Borlace S, Lengaigne M, Van Rensch P, Collins M, Vecchi G, et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat Clim Change. 2014;4(2):111-6. https://doi.org/10.1038/nclimate2100
- 7. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, et al. The DSSAT cropping system model. Eur J Agron. 2003;18(3-4):235-65. https://doi.org/10.1016/S1161-0301(02)00107-7
- 8. Lobell DB, Burke MB. On the use of statistical models to predict crop yield responses to climate change. Agric For Meteorol. 2010;150(11):1443-52. https://doi.org/10.1016/j.agrformet.2010.07.008
- 9. Meehl GA, Arblaster JM, Tebaldi C. Understanding future patterns of increased precipitation intensity in climate model simulations. Geophys Res Lett. 2005;32(18). https://doi.org/10.1029/2005GL023680
- 10. Tesfamariam BG, Gessesse B, Melgani F. Characterizing the spatiotemporal distribution of meteorological drought as a response to climate variability: The case of rift valley lakes basin of Ethiopia. Weather Clim Extremes. 2019;26:100237. https://doi.org/10.1016/j.wace.2019.100237
- 11. Nicholson SE. A detailed look at the recent drought situation in the Greater Horn of Africa. J Arid Environ. 2014;103:71-9. https://doi.org/10.1016/j.jaridenv.2013.12.003
- 12. Tall A, Coulibaly JY, Diop M. Do climate services make a difference? A review of evaluation methodologies and practices to assess the value of climate information services for farmers: Implications for Africa. Clim Serv. 2018;11:1-12. https://doi.org/10.1016/j.cliser.2018.06.001
- 13. Vermeulen SJ, Aggarwal PK, Ainslie A, Angelone C, Campbell BM, Challinor AJ, et al. Options for support to agriculture and food security under climate change. Environ Sci Policy. 2012;15(1):136-44. https://doi.org/10.1016/j.envsci.2011.09.003
- 14. Waha K, Müller C, Bondeau A, Dietrich JP, Kurukulasuriya P, Heinke J, et al. Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa. Glob Environ Change. 2013;23(1):130-43. https://doi.org/10.1016/j.gloenvcha.2012.11.001
- 15. Challinor A, Wheeler T, Garforth C, Craufurd P, Kassam A. Assessing the vulnerability of food crop systems in Africa to climate change. Clim Change. 2007;83(3):381-99. https://doi.org/10.1007/s10584-007-9249-0
- 16. Hansen JW, Challinor A, Ines A, Wheeler T, Moron V. Translating climate forecasts into agricultural terms: advances and challenges. Clim Res. 2006;33:27-41. https://doi.org/10.3354/cr033027
- 17. McCabe MF, Rodell M, Alsdorf DE, Miralles DG, Uijlenhoet R, Wagner W, et al. The future of Earth observation in hydrology. Hydrol Earth Syst Sci. 2017;21(7):3879-914. https://doi.org/10.5194/hess-21-3879-2017
- 18. Zhang G, Wu Y, Zhao W, Zhang J. Radar-based multipoint displacement measurements of a 1200-m-long suspension bridge. ISPRS J Photogramm Remote Sens. 2020;167:71-84. https://doi.org/10.1016/j.isprsjprs.2020.06.017
- 19. Roncoli C, Jost C, Kirshen P, Sanon M, Ingram KT, Woodin M, et al. From accessing to assessing forecasts: an end-to-end study of participatory climate forecast dissemination in Burkina Faso (West Africa). Clim Change. 2009;92:433-60. https://doi.org/10.1007/s10584-008-9445-6
- 20. Zougmoré RB, Partey ST, Totin E, Ouédraogo M, Thornton P, Karbo N, et al. Science-policy interfaces for sustainable climate-smart agriculture uptake: lessons learnt from national science-policy dialogue platforms in West Africa. Int J Agric Sustain. 2019;17(5):367-82. https://doi.org/10.1080/14735903.2019.1670934
- 21. Washington R, Harrison M, Conway D, Black E, Challinor A, Grimes D, et al. African climate change: taking the shorter route. Bull Am Meteorol Soc. 2006;87(10):1355-66. https://doi.org/10.1175/BAMS-87-10-1355
- 22. Singh C, Tebboth M, Spear D, Ansah P, Mensah A. Exploring methodological approaches to assess climate change vulnerability and adaptation: reflections from using life history approaches. Reg Environ Change. 2019;19(8):2667-82. https://doi.org/10.1007/s10113-019-01562-z
- 23. Achli S, Epule TE, Dhiba D, Salih W, Chehbouni A. Vulnerability of maize, barley and wheat yields to growing season temperature and socioeconomic indicators in Morocco. J Water Clim Change. 2024;15(4):1588-611. https://doi.org/10.2166/wcc.2024.498
- 24. Kristjanson P, Neufeldt H, Gassner A, Mango J, Kyazze FB, Desta S, et al. Are food insecure smallholder households making changes in their farming practices? Evidence from East Africa. Food Sec. 2012;4(3):381-97. https://doi.org/10.1007/s12571-012-0194-z
- 25. Roncoli C, Ingram K, Kirshen P. Reading the rains: Local knowledge and rainfall forecasting in Burkina Faso. Soc Nat Resour. 2002;15(5):409-27. https://doi.org/10.1080/08941920252866774
- 26. Harvey CA, Rakotobe ZL, Rao NS, Dave R, Razafimahatratra H, Rabarijohn RH, et al. Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar. Philos Trans R Soc B Biol Sci. 2014;369(1639):20130089. https://doi.org/10.1098/rstb.2013.0089
- 27. Islam N, Winkel J. Climate change and social inequality. New York (NY): United Nations, Department of Economic and Social Affairs (DESA); 2017.
- 28. Fader M, Gerten D, Krause M, Lucht W, Cramer W. Spatial decoupling of agricultural production and consumption: quantifying dependences of countries on food imports due to domestic land and water constraints. Environ Res Lett. 2013;8(1):014046. https://doi.org/10.1088/1748-9326/8/1/014046
- 29. Nelson GC, Valin H, Sands RD, Havlík P, Ahammad H, Deryng D, et al. Climate change effects on agriculture: Economic responses to biophysical shocks. Proc Natl Acad Sci U S A. 2014;111(9):3274-9. https://doi.org/10.1073/pnas.1222465110
- 30. Tziperman E, Zebiak SE, Cane MA. Mechanisms of seasonal-ENSO interaction. J Atmos Sci. 1997;54(1):61-71. https://doi.org/10.1175/1520-0469(1997)054%3C0061:MOSEI%3E2.0.CO;2
- 31. Akhmet M, Fen MO, Alejaily EM. The Effects of El Niño on the global weather and climate. arXiv preprint arXiv:180100891. 2018.
- 32. Ineson S, Scaife A. The role of the stratosphere in the European climate response to El Niño. Nat Geosci. 2009;2(1):32-6. https://doi.org/10.1038/ngeo381
- 33. Butler AH, Polvani LM, Deser C. Separating the stratospheric and tropospheric pathways of El Niño-Southern Oscillation teleconnections. Environ Res Lett. 2014;9(2):024014. https://doi.org/10.1088/1748-9326/9/2/024014
- 34. Kämpf J. Do coastal or equatorial wind anomalies drive the Indian Ocean Dipole? J Mar Syst. 2024;246:104016. https://doi.org/10.1016/j.jmarsys.2024.104016
- 35. Ratna SB, Cherchi A, Osborn TJ, Joshi M, Uppara U. The extreme positive indian ocean dipole of 2019 and associated indian summer monsoon rainfall response. Geophys Res Lett. 2021;48(2):e2020GL091497. https://doi.org/10.1029/2020GL091497
- 36. Ropelewski CF, Halpert MS. Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon Weather Rev. 1987;115(8):1606-26. https://doi.org/10.1175/1520-0493(1987)115%3C1606:GARSPP%3E2.0.CO;2
- 37. Hurrell JW. Decadal trends in the North Atlantic oscillation: Regional temperatures and precipitation. Science. 1995;269(5224):676-9. https://doi.org/10.1126/science.269.5224.676
- 38. Hurrell JW, Deser C. North Atlantic climate variability: The role of the North Atlantic Oscillation. J Mar Syst. 2010;79(3):231-44. https://doi.org/10.1016/j.jmarsys.2009.11.002
- 39. Walker G, Bliss E. Memoirs of the royal meteorological society. World weather V. 1932;4:53-84.
- 40. Iglesias A, Garrote L, Quiroga S, Moneo M. A regional comparison of the effects of climate change on agricultural crops in Europe. Clim Change. 2012;112:29-46.
- 41. Krishna Kumar K, Kumar RK, Ashrit R, Deshpande N, Hansen JW. Climate impacts on Indian agriculture. Int J Climatol. 2004;24(11):1375-93. https://doi.org/10.1002/joc.1081
- 42. Kumar KK, Rajagopalan B, Cane MA. On the weakening relationship between the Indian Monsoon and ENSO. Science. 1999;284(5423):2156-9. https://doi.org/10.1126/science.284.5423.2156
- 43. Cai W, Cowan T, Sullivan A. Recent unprecedented skewness towards positive Indian Ocean Dipole occurrences and its impact on Australian rainfall. Geophys Res Lett. 2009;36(11). https://doi.org/10.1029/2009GL037604
- 44. Funk C, Dettinger MD, Michaelsen JC, Verdin JP, Brown ME, Barlow M, et al. Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. Proc Natl Acad Sci U S A. 2008;105(32):11081-6. https://doi.org/10.1073/pnas.0708196105
- 45. Trnka M, Feng S, Semenov MA, Olesen JE, Kersebaum KC, Rötter RP, et al. Mitigation efforts will not fully alleviate the increase in water scarcity occurrence probability in wheat-producing areas. Sci Adv. 2019;5(9):eaau2406. https://doi.org/10.1126/sciadv.aau2406
- 46. Handler P, Andsager K. Volcanic aerosols, El Niño and the Southern Oscillation. Int J Climatol. 1990;10(4):413-24. https://doi.org/10.1002/joc.3370100409
- 47. Baethgen WE. Vulnerability of the agricultural sector of Latin America to climate change. Clim Res. 1997;9(1-2):1-7. https://doi.org/10.3354/cr009001
- 48. Santer BD, Wigley T, Barnett TP, Anyamba E, Bloomfield P, Cook E, et al. Detection of climate change and attribution of causes. In: Climate Change 1995: The Science of Climate Change: Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge (UK): Cambridge University Press; 1996. p. 411-39.
- 49. Monteleone B, Borzí I, Bonaccorso B, Martina M. Quantifying crop vulnerability to weather-related extreme events and climate change through vulnerability curves. Nat Hazards. 2023;116(3):2761-96. https://doi.org/10.1007/s11069-022-05791-0
- 50. Wahid A, Gelani S, Ashraf M, Foolad MR. Heat tolerance in plants: an overview. Environ Exp Bot. 2007;61(3):199-223. https://doi.org/10.1016/j.envexpbot.2007.05.011
- 51. Jagadish SK, Craufurd PQ, Wheeler T. High temperature stress and spikelet fertility in rice (Oryza sativa L.). J Exp Bot. 2007;58(7):1627-35. https://doi.org/10.1093/jxb/erm003
- 52. Flexas J, Medrano H. Drought-inhibition of photosynthesis in C3 plants: stomatal and non-stomatal limitations revisited. Ann Bot. 2002;89(2):183-9. https://doi.org/10.1093/aob/mcf027
- 53. Tardieu F, Davies W. Integration of hydraulic and chemical signalling in the control of stomatal conductance and water status of droughted plants. Plant Cell Environ. 1993;16(4):341-9. https://doi.org/10.1111/j.1365-3040.1993.tb00880.x
- 54. Ashraf M, Foolad MR. Roles of glycine betaine and proline in improving plant abiotic stress resistance. Environ Exp Bot. 2007;59(2):206-16. https://doi.org/10.1016/j.envexpbot.2005.12.006
- 55. Mittler R. Oxidative stress, antioxidants and stress tolerance. Trends Plant Sci. 2002;7(9):405-10. https://doi.org/10.1016/S1360-1385(02)02312-9
- 56. Hsiao J, Swann AL, Kim S-H. Maize yield under a changing climate: The hidden role of vapor pressure deficit. Agric For Meteorol. 2019;279:107692. https://doi.org/10.1016/j.agrformet.2019.107692
- 57. Senthilnathan S, Benson D, Prasanna V, Mallick T, Thiyagarajan A, Ramasamy M, et al. Impact of climate variability on Maize yield under different climate change scenarios in Southern India: A Panel Data Approach. Earth. 2025;6(1):16. https://doi.org/10.3390/earth6010016
- 58. Su P, Li S, Wang Ja, Liu F. Vulnerability assessment of Maize yield affected by precipitation fluctuations: A Northeastern United States case study. Land. 2021;10(11):1190. https://doi.org/10.3390/land10111190
- 59. Shuai J, Zhang Z, Tao F, Shi P. How ENSO affects maize yields in China: understanding the impact mechanisms using a process-based crop model. Int J Climatol. 2016;36(1). https://doi.org/10.1002/joc.4360
- 60. Epule TE, Chehbouni A, Dhiba D, Etongo D, Driouech F, Brouziyne Y, et al. Vulnerability of Maize, millet and rice yields to growing season precipitation and socio-economic proxies in Cameroon. PLoS One. 2021;16(6):e0252335. https://doi.org/10.1371/journal.pone.0252335
- 61. Sazib N, Mladenova lE, Bolten JD. Assessing the impact of ENSO on agriculture over Africa using Earth Observation Data. Front Sustain Food Syst. 2020;4:509914. https://doi.org/10.3389/fsufs.2020.509914
- 62. Khedwal RS, Chaudhary A, Sindhu VK, Yadav DB, Kumar N, Chhokar RS, et al. Challenges and technological interventions in rice-wheat system for resilient food-water-energy-environment nexus in North-western Indo-Gangetic Plains: A review. Cereal Res Commun. 2023:1-23. https://doi.org/10.1007/s42976-023-00355-9
- 63. Le T, Bae DH. Causal impacts of El Niño-Southern Oscillation on global soil moisture over the period 2015-2100. Earths Future. 2022;10(3):e2021EF002522. https://doi.org/10.1029/2021EF002522
- 64. Cai W, van Rensch P, Cowan T, Hendon HH. Teleconnection pathways of ENSO and the IOD and the mechanisms for impacts on Australian rainfall. J Clim. 2011;24(15):3910-23. https://doi.org/10.1175/2011JCLI4129.1
- 65. Wu C, Yeh PJF, Wu H, Hu BX, Huang G. Global analysis of the role of terrestrial water storage in the evapotranspiration estimated from the Budyko framework at annual to monthly time scales. J Hydrometeorol. 2019;20(10):2003-21. https://doi.org/10.1175/JHM-D-19-0065.1
- 66. Yin H, Wu Z, Fowler HJ, Blenkinsop S, He H, Li Y. The combined impacts of ENSO and IOD on global seasonal droughts. Atmosphere. 2022;13(10):1673. https://doi.org/10.3390/atmos13101673
- 67. Yan J, Zhang W, Hu S, Jiang F. Different ENSO impacts on eastern China precipitation patterns in early and late winter associated with seasonally-varying kuroshio anticyclonic anomalies. Adv Atmos Sci. 2024;41(9):1691-703. https://doi.org/10.1007/s00376-023-3196-1
- 68. Niu J, Chen J, Sivakumar B. Teleconnection analysis of runoff and soil moisture over the Pearl River basin in southern China. Hydrol Earth Syst Sci. 2014;18(4):1475-92. https://doi.org/10.5194/hess-18-1475-2014
- 69. Chauhan AS, Rani A, Dahiya P, Maurya RKS, Danodia A. Understanding the influence of teleconnections on Indian summer monsoon rainfall and agricultural productivity: a case study of Haryana, India (1980-2023). J Water Clim Change. 2024;15(12):5793-816. https://doi.org/10.2166/wcc.2024.311
- 70. Makarieva AM, Nefiodov AV, Nobre AD, Baudena M, Bardi U, Sheil D, et al. The role of ecosystem transpiration in creating alternate moisture regimes by influencing atmospheric moisture convergence. Glob Change Biol. 2023;29(9):2536-56. https://doi.org/10.1111/gcb.16644
- 71. Trigo R, Osborn T, Corte-Real J. The North Atlantic oscillation influence on Europe: climate impacts and associated physical mechanisms. Clim Res. 2002;20:9-17. https://doi.org/10.3354/cr020009
- 72. Challinor AJ, Watson J, Lobell DB, Howden SM, Smith D, Chhetri N. A meta-analysis of crop yield under climate change and adaptation. Nat Clim Change. 2014;4(4):287-91. https://doi.org/10.1038/nclimate2153
- 73. Holzworth DP, Huth NI, deVoil PG, Zurcher EJ, Herrmann NI, McLean G, et al. APSIM-evolution towards a new generation of agricultural systems simulation. Environ Model Softw. 2014;62:327-50. https://doi.org/10.1016/j.envsoft.2014.07.009
- 74. Lobell DB, Asseng S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ Res Lett. 2017;12(1):015001. https://doi.org/10.1088/1748-9326/aa518a
- 75. Martre P, Wallach D, Asseng S, Ewert F, Jones JW, Rötter RP, et al. Multimodel ensembles of wheat growth: many models are better than one. Glob Change Biol. 2015;21(2):911-25. https://doi.org/10.1111/gcb.12768
- 76. Lobell DB. Climate change adaptation in crop production: Beware of illusions. Glob Food Sec. 2014;3(2):72-6. https://doi.org/10.1016/j.gfs.2014.05.002
- 77. Challinor A, Slingo J, Wheeler T, Craufurd P, Grimes D. Toward a combined seasonal weather and crop productivity forecasting system: determination of the working spatial scale. J Appl Meteorol. 2003;42(2):175-92. https://doi.org/10.1175/1520-0450(2003)042%3C0175:TACSWA%3E2.0.CO;2
- 78. Kuwata K, Shibasaki R. Estimating corn yield in the united states with modis evi and machine learning methods. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. 2016;3:131-6. https://doi.org/10.5194/isprs-annals-III-8-131-2016
- 79. Hochman Z, Holzworth D, Hunt JR. Potential to improve on-farm wheat yield and WUE in Australia. Crop Pasture Sci. 2009;60(8):708-16. https://doi.org/10.1071/CP09064
- 80. Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, et al. Uncertainty in simulating wheat yields under climate change. Nat Clim Change. 2013;3(9):827-32. https://doi.org/10.1038/nclimate1916
- 81. Thornton PK, Ericksen PJ, Herrero M, Challinor AJ. Climate variability and vulnerability to climate change: a review. Glob Change Biol. 2014;20(11):3313-28. https://doi.org/10.1111/gcb.12581
- 82. Goli I, Kriaučiūnienė Z, Zhang R, Bijani M, Koohi PK, Rostamkalaei SA, et al. Contributions of climate smart agriculture toward climate change adaptation and food security: The case of Mazandaran Province, Iran. Trends Food Sci Technol. 2024:104653. https://doi.org/10.1016/j.tifs.2024.104653
- 83. Ellis E, Paustian K. Importance of on-farm research for validating process-based models of climate-smart agriculture. Carbon Balance Manag. 2024;19(1):16. https://doi.org/10.1186/s13021-024-00260-6
- 84. Zhang X, Davidson EA, Mauzerall DL, Searchinger TD, Dumas P, Shen Y. Managing nitrogen for sustainable development. Nature. 2015;528(7580):51-9. https://doi.org/10.1038/nature15743
- 85. Hunsaker DJ, Bronson KF. FAO56 crop and water stress coefficients for cotton using subsurface drip irrigation in an arid US climate. Agric Water Manag. 2021;252:106881. https://doi.org/10.1016/j.agwat.2021.106881
- 86. Verchot LV, Van Noordwijk M, Kandji S, Tomich T, Ong C, Albrecht A, et al. Climate change: linking adaptation and mitigation through agroforestry. Mitig Adapt Strateg Glob Change. 2007;12:901-18. https://doi.org/10.1007/s11027-007-9105-6
- 87. Incoom ABM, Adjei KA, Odai SN, Siabi EK, Donkor P, Frimpong K. Adaptation strategies by smallholder farmers to climate change and variability: The case of the savannah zone of Ghana. Sustain Futures. 2025;9:100543. https://doi.org/10.1016/j.sftr.2025.100543
- 88. Maher N, Wills RCJ, DiNezio P, Klavans J, Milinski S, Sanchez SC, et al. The future of the El Niño-Southern oscillation: using large ensembles to illuminate time-varying responses and inter-model differences. Earth Syst Dyn. 2023;14(2):413-31. https://doi.org/10.5194/esd-14-413-2023
- 89. Sirmacek B, Vinuesa R. Remote sensing and AI for building climate adaptation applications. Results Eng. 2022;15:100524. https://doi.org/10.1016/j.rineng.2022.100524
- 90. Baraj B, Mishra M, Sudarsan D, da Silva RM, Santos CAG. Climate change and resilience, adaptation and sustainability of agriculture in India: A bibliometric review. Heliyon. 2024. https://doi.org/10.1016/j.heliyon.2024.e29586
- 91. Das A, Rewari S, Kanaujia BK, Deswal S, Gupta R. Physics based numerical model of a nanoscale dielectric modulated step graded germanium source biotube FET sensor: modelling and simulation. Phys Scr. 2023;98(11):115013. https://doi.org/10.1088/1402-4896/acf4c9
- 92. Hansen JW, Mason SJ, Sun L, Tall A. Review of seasonal climate forecasting for agriculture in sub-Saharan Africa. Exp Agric. 2011;47(2):205-40. https://doi.org/10.1017/S0014479710000876
- 93. Van Klompenburg T, Kassahun A, Catal C. Crop yield prediction using machine learning: A systematic literature review. Comput Electron Agric. 2020;177:105709. https://doi.org/10.1016/j.compag.2020.105709
- 94. Nyong A, Adesina F, Osman Elasha B. The value of indigenous knowledge in climate change mitigation and adaptation strategies in the African Sahel. Mitig Adapt Strateg Glob Change. 2007;12:787-97. https://doi.org/10.1007/s11027-007-9099-0
- 95. Greatrex H, Hansen J, Garvin S, Diro R, Le Guen M, Blakeley S, et al. Scaling up index insurance for smallholder farmers: Recent evidence and insights. Greatrex H, Hansen J, Garvin S, Diro R, Blakeley S, Le Guen M, et al. Scaling up index insurance for smallholder farmers: recent evidence and insights [Internet]. CCAFS Report No. 14. Copenhagen (Denmark): CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS); 2015 [cited 2025 Nov 30]. https://hdl.handle.net/10568/53101
- 96. Antwi-Agyei P, Dougill AJ, Agyekum TP, Stringer LC. Alignment between nationally determined contributions and the sustainable development goals for West Africa. Clim Policy. 2018;18(10):1296-312. https://doi.org/10.1080/14693062.2018.1431199
Downloads
Download data is not yet available.