Climate variability and corn yield in Argentina: the role of El Niño–Southern Oscillation index and other climate drivers

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Fernández-Long, Marcos Texeira, Roberto J. Fernández, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6269402/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Oct, 2025 Read the published version in Theoretical and Applied Climatology → Version 1 posted 9 You are reading this latest preprint version Abstract Understanding how climate variability influences corn yield is essential for improving agricultural forecasting and resilience strategies. This study examines the role of four major climate indices—El Niño Southern Oscillation (ENSO) index related ONI, IOD, AAO and TSA—on corn yield variability across Argentina's diverse agro-ecological regions (22° to 39° S latitude). Using historical rainfed yield data at the county level and monthly climate records (1994–2024), we assessed the temporal and spatial relationships between these indices and detrended yield anomalies. ONI emerged as the dominant predictor, particularly in the core production region of the country, where its positive phase correlated strongly with increased rainfall during critical crop growth stages. IOD also exerted a significant influence, although it was more localized and seasonally dependent, primarily affecting mid-summer precipitation patterns at the core region. Conversely, the effects of AAO and TSA were weaker and even more regionally constrained (for Cordoba and coastal areas, respectively). A Principal Component Analysis (PCA) including all four indexes reinforced these findings, highlighting the combined and overlapping influence of climate indices rather than simple relationships. Notably, the absence of a clear regional differentiation in the PCA suggests that climate signals act broadly across Argentina, making it difficult to generalize yield predictions based on any single index. These results challenge the conventional assumption that ENSO alone can reliably forecast yield outcomes and underline the necessity of multi-index approaches for robust agricultural modeling. By integrating climate indices into predictive frameworks, this research could enhance yield forecasting accuracy, supporting adaptive management strategies in the face of increasing climate variability. Yield forecasting Teleconnections Rainfed agriculture Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights ONI is the dominant predictor, particularly in the core production region of the country. IOD also exerts a significant influence, although it is more localized and seasonal. The effects of AAO and TSA are weaker and even more regionally constrained. The results underline the necessity of multi-index approaches for enhance yield forecasting accuracy. 1. Introduction In Argentina, corn yields are significantly influenced by diverse climatic interactions across varying agro-ecological zones (e.g. Ray et al., 2015 ; Casali et al., 2022 ). Rainfall is essential for corn production; insufficient water during critical growth stages such as flowering and grain filling can lead to severe yield reductions, while excessive rainfall can result in waterlogging, heightened disease risk, and root damage (Lobell & Field, 2007 ; Lobell & Gourdji, 2012 ). Another driver is temperature: moderate warm weather can stimulate growth, but warmer temperatures can shorten the crop cycle, and extreme heat during flowering disrupts reproduction, leading to significant losses of yield (Deryng et al., 2014 ). Equally important is soil moisture - largely influenced by rainfall, evapotranspiration, texture and soil depth (Trenberth et al., 2014 ). Together, these factors underscore the need for more precise climate forecasting, enabling adaptive strategies that strengthen productivity and resilience under changing climate conditions, particularly in a context where an increase in drought frequency, severity, and extreme temperatures is projected over southern South America (Spennemann et al., 2024 ). Different modes of variability have been identified as tools to simplify the understanding of the climate system by distilling its complexity into observable and predictable patterns (Hernández et al., 2020 ). In turn, climate indices condense complex atmospheric and oceanic interactions into patterns that can be associated with climatic shifts over time. Although these indices do not provide direct forecasts of variables like rainfall or temperature, they reveal broader atmospheric patterns that correlate with these drivers, offering stable and interpretable signals for long-term climate assessments (Zebiak & Cane, 1987 ). By capturing large-scale atmospheric dynamics, climate indices provide valuable insights for rainfed agricultural systems, where regional and seasonal climatic variations largely determine yield outcomes (Feng et al., 2020 ; Karimzadeh Soureshjani, 2021 ). Integrating these indices into yield prediction models allows to understand the influence of climatic drivers on crop performance over time and space, underscoring their critical role in supporting agriculture under increasingly variable climatic conditions (Lalić et al., 2014 ; Mathieu & Aires, 2018 ). Analyzing multiple climate indices over time and space has allowed to better understand the complex interactions that drive corn yield variability all around the world (e.g. Meyer et al., 1991 ; Jozami et al., 2018 ; Zhang et al., 2020 ). The use of indices that quantify the ENSO phenomenon (El Niño-Southern Oscillation), such as ONI (Ocean Niño Index, Trenberth 1997 ), and IOD (Indian Ocean Dipole, Saji et al. 1999 ), TSA (Tropical Southern Atlantic Index, Enfield et al. 1999 ), or AAO (Antarctic Oscillation, Thompson & Wallace 2000 ) has provided valuable insights into the temporal and regional effects on yield outcomes (Pol & Binyamin, 2014 ; Nguyen-Huy et al., 2018 ; de la Casa et al., 2021 ). ENSO-induced rainfall anomalies, for example, have a direct impact on corn yields, particularly during critical growth phases (e.g. Gimms et al., 2000; Podestá et al., 2002). IOD-induced precipitation changes are often context-dependent, varying according to local atmospheric dynamics and their interactions with other indices (Saji et al., 1999 ; Ashok et al., 2001 ). Similarly, TSA and AAO influence temperature and moisture distribution, affecting crop development (Venegas et al., 1997 ; Silvestri & Vera, 2003 ). While indices have limitations --such as ENSO’s broad-scale influence and IOD’s inconsistent predictability– their integration into predictive models has improved yield forecasts, enabling more informed decision-making (Iizumi et al., 2018 ; Cao et al., 2023 ). In this context, this study aims to enhance the predictive understanding of key climate indices – such us ONI, IOD, TSA, and AAO --for corn yield across Argentina’s diverse agro-ecological regions. We explored three critical questions regarding the predictive power of each index: 1) How accurately can this index forecast yield variability? 2) During which stages of the growing season are its predictions most reliable? and 3) In which regions of Argentina do each index demonstrate the greatest predictive strength? By integrating the forecasting capabilities of these indices, our analysis captured both temporal and spatial variability, accounting for regional differences and interannual fluctuations. Given Argentina’s vast and heterogeneous landscape, refining yield predictions can enable more effective management strategies, better resource allocation, and proactive risk mitigation. 2. Materials and methods 2.1. Study area Our study focused on 171 counties (locally known as partidos or departamentos) within Argentina’s primary corn-producing regions: northwest of Buenos Aires province (n = 16), northeast of Buenos Aires province (n = 29), southwest of Buenos Aires province (n = 12), southeast of Buenos Aires province (n = 19), Chaco province (n = 18), Córdoba province (n = 19), Entre Rios province (n = 15), Jujuy province (n = 3), La Pampa province (n = 13), Misiones province (n = 7), Salta province (n = 6) and Santa Fe province (n = 14) (Fig. 1 ). These cover 71 million hectares, spanning from − 22° latitude in the north to -39° in the south, and from − 66° longitude in the west to -54° in the east, thus encompassing diverse climatic zones, soil types, topographies, and management practices (Bert et al., 2006 ; Calviño & Monzon, 2009 ; Cabrini et al., 2019 ; Rubio et al., 2019 ; BAGE, 2024 ). Corn ( Zea mays L .) is one of the dominant crops in Argentina, with an average annual production of around 55 million tons, sown across over 10 million hectares each year (MAGyP 2024 ). The whole region features two distinct Southern-Hemisphere Spring and Summer sowing periods: early planting, typically occurring from late September to early October, and late planting, from December to early January (Maddonni, 2012 ; Otegui et al., 2021 ). Early planting is more common in the central and northern parts of the region, while late planting predominates towards the south and west (de Abelleyra et al., 2024 ), resulting in a varied exposure to climatic risks such as rainfall shortages, heat stress and excessive moisture during different growth stages. 2.2. Climatological information To analyze the relationship between the spatio-temporal variability of climate and corn yields, we employed four key climate indices: ONI (Ocean Niño Index), TSA (Tropical Southern Anomaly), AAO (Antarctic Oscillation), and IOD (Indian Ocean Dipole) (Table 1 ). These indices were extracted monthly for each county from July 1994 to May 2024, capturing interannual climate variability across the 30-yr study period. The ONI index, a widely recognized measure of ENSO variability, has been extensively used to study the impacts of El Niño events on regional climates (Trenberth, 1997 ; Fernández Long et al. 2011 ; Jozami et al. 2018 ). The TSA index, reflecting sea surface temperature anomalies in the South Atlantic, is crucial for understanding modulating rainfall patterns over South America (Andreoli & Kayano, 2006 ; Kayano et al., 2009 ; Reboita et al., 2021 ; Santos et al., 2024 ). The AAO serves as an important indicator of atmospheric circulation in the Southern Hemisphere, reflecting both temperature and precipitation patterns (Gong & Wang, 1999 ; Thompson & Wallace, 2000 ). Lastly, the IOD index provides insights into rainfall anomalies originating from the Indian Ocean, with downstream effects on South American climates (Saji et al., 1999 ). Table 1 Climate indices used and their data sources iNDICES DETAILS LINK ONI Oceanic Niño Index One of the most widely used ENSO indices. Calculated as the 3-month running mean of ERSST.v5 SST anomalies in the Niño 3.4 region (5°N–5°S, 120°W–170°W), based on centered 30-year base periods updated every 5 years, beginning in 1950. https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt tsa Tropical Southern Atlantic Index It is an indicator of the surface temperatures in the Gulf of Guinea, the eastern tropical South Atlantic. Uses the anomaly of the average of the monthly SST from Eq-20S and 10E-30W from HadISST and NOAA OI 1x1 datasets. Climatology is? Spans from? 1971–2000. https://psl.noaa.gov/data/correlation/tsa.data iod Indian Ocean Dipole Proxy for a climate pattern affecting the Indian Ocean, calculated as the difference in sea surface temperature (SST) anomalies between two regions in the equatorial Indian Ocean: Western (50°E – 70°E, 10°S – 10°N) and Eastern (90°E – 110°E, 10°S – 0°). YEARS COVERED??? https://sealevel.jpl.nasa.gov/data/vital-signs/indian-ocean-dipole/ aao Antarctic Oscillation The AAO or Southern Annular Mode (SAM) is the dominant pattern of natural variability in the Southern Hemisphere outside the tropics. Constructed by projecting the daily (00Z) 700-hPa height anomalies poleward of 20°S onto the leading pattern of the AAO. YEARS COVERED?? https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/aao/aao.shtml 2.3. Corn Production Information Corn yield data were collected at the county level, covering the 1994/1995 to 2023/2024 growing seasons (Southern Hemisphere - Spring + Summer). The data were sourced from the official databases of the “ Ministerio de Agricultura, Ganadería y Pesca of Argentina” (MAGYP 2024 ). These are average county yields from rainfed, commercial (non-experimental) fields. To isolate the effects of climatic factors on yield variability, we applied a de-trending process to remove the influence of temporal trends linked to technological advancements, including genetic improvements, new machinery and chemicals, and enhanced management practices (Vossen 1989; Hough 1990). Previous work demonstrated that corn yields in Argentina showed a constant gain over the years (Gear, 2006 ; Rotilli et al., 2019 ; Maddonni et al., 2021 ). Therefore, linear time trends were calculated for each county. Then, the deviation of corn yields (MYD i ) was calculated as the difference between the corn yield (MY) of a year (i) minus the linear trend (Trend i ) for that year (Eq. 1 ): $$\:{MYD}_{i}={MY}_{i}-{Trend}_{i}$$ 1 2.4. Statistical analysis Climate variability as captured by the four indices and corn yield data were analyzed both temporally and spatially using Spearman rank correlation (Quinn and Keough 2002 ). Similar methods have been widely employed in agricultural yield studies to establish climate-yield relationships (Ferreira et al. 2001; Ray et al., 2015 ; Fernández Long et al. 2025). The association and joint relationships among standardized climatic indices and corn yield anomalies (see above) were assessed by means of Principal Component Analysis (PCA, Legendre and Legendre 1998 ) and correlation biplot scaling of PCA ordination plots (Legendre and Legendre 1998 ; Borcard and others 2011). In this type of PCA scaling, the angles between variables or descriptors (the indices in different months and the corn yield anomalies.) reflect their correlations across the counties; angles from 0º to 90º represent decreasing positive correlations, whereas angles from 90º to 180º represent increasing negative ones. In turn, angles around 90º represent uncorrelated descriptors. 3. Results and Discussion ONI index, representing ENSO variability, exhibited significant positive detrended temporal correlations (p-value < 0.10 to p-value < 0.01) with corn yield anomalies in central Argentina, particularly for Northern Buenos Aires, Santa Fe, and Entre Ríos provinces. Correlation values ranged from 0.30 to 0.69 (Fig. 2 -A), predominantly throughout the productive cycle (June to March, i.e. winter through early fall) (Fig. 2 -B), with the strongest associations occurring for December-January (summer) central-east planting zone (Argentina Corn Belt), there coinciding with maximum vegetative growth and critical grain-filling stage, respectively (Fig. 2 A,B,C).This alignment underlines the importance of water availability for these rain-fed corn systems, as highlighted by Podestá et al. (2002). However, in regions to the south and west such as La Pampa and southern Buenos Aires, correlations were weaker (r < 0.50) and observed only in isolated counties without a clear temporal pattern (Fig. 2 ; Supplementary Materials Table 1). In turn, the northern provinces of Chaco, Salta, Jujuy, and Misiones showed no significant ENSO-related effects on corn yield. It is important to highlight that in the Córdoba province the strongest correlations occur for the spring months of September-October, in contrast to the rest of the core corn region (Buenos Aires, Santa Fe and Entre Ríos) (Fig. 2 B). This pattern is very likely driven by the region’s soil moisture deficit, which is typically most pronounced in October (Fernández Long et al., 2020). Soil moisture availability at the beginning of the growing season is crucial for supporting early crop development, ultimately contributing to improved yield outcomes. The Indian Ocean Dipole (IOD) exhibited significant positive correlations (p-value < 0.1 to p-value < 0.01) in several regions of Argentina, particularly between December and March, aligning with corn's critical growth stages, including flowering and grain filling (Fig. 3 A, B, C). However, the spatial distribution of correlations indicates that, while the IOD relates to rainfall patterns during this period, its impact is more localized and less extensive compared to that of the ONI. This suggests that the IOD reflects mid-season rainfall variability rather than being related with annual precipitation trends. Notably, the highest correlations were concentrated in central and eastern Argentina, whereas western and northern regions showed weaker or non-significant responses (with a few counties with negative ones). These findings align with previous studies highlighting the role of the IOD in modulating summer precipitation anomalies in South America, particularly through its interactions with other large-scale climatic drivers (Saji et al., 1999 ; Cai et al., 2009 ). The Antarctic Oscillation index (AAO) exhibited a predominantly negative correlation with corn yields in Argentina, with significant figures (p-value < 0.1 to p-value < 0.01) concentrated between November and January across the core corn-growing region, particularly in northern Buenos Aires and Entre Ríos (Fig. 4 A, B, C). For Córdoba, the most pronounced negative correlations were observed in January of year + 1 , affecting most counties in the province. In contrast, other provinces showed no significant association between AAO variability and corn yield anomalies, reinforcing its more regionally specific and seasonally constrained impact. These findings align with previous studies indicating that while the AAO can modulate mid-latitude circulation patterns, its influence on rainfall anomalies in the Argentine Pampas is limited compared to stronger drivers such as ENSO or IOD (Thompson & Wallace, 2000 ; Silvestri & Vera, 2003 ). The mechanism behind this weaker influence likely stems from the AAO’s response to shifting the position and intensity of the subtropical jet stream, which primarily affects precipitation patterns in southern Argentina (outside our maps) rather than the central-north agricultural areas (Kidson, 1988; Gong & Wang, 1999 ). Although the AAO can influence the passage of cold fronts and transient weather systems, its effect on sustained precipitation anomalies critical for corn growth remains secondary to more dominant climatic drivers. The South Atlantic Ocean Anomaly (TSA) exhibited no significant correlation with corn yields across most of the analyzed regions in Argentina (Fig. 5 A, B, C), with only a few localized exceptions. A weak but consistent negative effect was detected during the winter months for Entre Ríos and Santa Fe, while Córdoba and Buenos Aires (North and South) showed a moderate regional impact during the same period. This aligns with previous studies that indicate the TSA’s limited role in modulating climatic variability over subtropical South America, particularly during the corn-growing season (Venegas et al., 1997 ). The TSA primarily affects sea surface temperature anomalies and atmospheric circulation over the South Atlantic, yet its downstream effects on precipitation and temperature anomalies in Argentina’s interior regions appear minimal when compared to more dominant climate drivers such as ENSO or IOD. One possible explanation for this negligible impact is the TSA’s weaker ability to generate persistent atmospheric anomalies that extend into the continent (Robertson & Mechoso, 2000; Bombardi & Carvalho, 2009). While the TSA can influence moisture advection from the Atlantic, its effect on regional precipitation is often masked by larger-scale atmospheric patterns. This would explain the TSA SOMEWHAT more significant influence on coastal rainfall patterns, with diminishing effects further inland, making it a less relevant predictor for corn yield variability in Argentina’s core agricultural areas. Consequently, when designing / building predictive models for corn yields, reliance on TSA alone may introduce unnecessary noise, which illustrates the importance of prioritizing indices with stronger and more consistent influences on the local climate. In sum, the ONI index exhibited the strongest correlations across the largest number of analyzed counties, reinforcing ENSO’s well-documented influence on interannual precipitation variability in southeastern South America (Boulanger et al., 2005; Barros et al., 2008; Cavalcanti et al., 2015; Andreoli et al., 2016; Iacovone et al., 2020). The highest ONI correlations were observed for January, a period that coincides with corn's critical reproductive phase, when water availability, also influenced by December precipitation is most crucial for yield determination (Fig. 6 ). However, despite ENSO’s prominent influence, it does not fully explain rainfall variability, as inferred from crop yield anomalies. The Indian Ocean Dipole (IOD) also plays a significant role, particularly in December, when its positive phase enhances precipitation in southeastern South America, while its negative phase is linked to drought conditions, consistent with our findings (Gonzalez & Vera, 2010; Chan et al., 2008; Sena & Magnusdottir, 2021). In contrast, the Antarctic Oscillation (AAO) showed its strongest correlations in November, aligning with previous studies that identified its influence on precipitation patterns during late spring (Silvestri & Vera, 2003 ). Negative AAO phases have been linked to positive precipitation anomalies in the region (Vera & Osman, 2018), further emphasizing the importance of this index in shaping early-season water availability. Meanwhile, the South Atlantic Ocean Anomaly (TSA) displayed its most significant correlations in June, suggesting a potential influence on pre-season moisture conditions, although its overall effect remained weaker compared to both ENSO and IOD. A principal component analysis (PCA) was performed to explore the complex interactions between climate indices (ENSO, IOD, AAO, and TSA) and maize yield anomalies, aiming to identify dominant patterns and joint drivers across Argentina's agro-ecological regions. The first two principal components (PC1 and PC2) together explained 63.58% of the total variance, with PC1 accounting for 46.52%, predominantly driven by ONI (winter and spring) and IOD (summer), reinforcing their critical role in shaping precipitation patterns and potentially temperature fluctuations during key growth phases such as flowering and grain filling. Meanwhile, PC2 (17.06%) was mainly associated with negative correlations of AAO and TSA, particularly in late spring and early summer, indicating their more localized and limited influence on yield variability (Fig. 7 ). The placement of maize yield anomalies in the biplot, positioned near ONI and IOD, highlights their stronger and more consistent impact on interannual yield fluctuations, while the weaker contributions of AAO and TSA suggest that their effects are either more regional or modulated by seasonal variability. Notably, the lack of clear spatial differentiation in PCA results suggests that maize yield responses to these indices are broadly consistent across Argentina, despite significant ecological diversity. These findings underscore the importance of a multi-index approach, integrating various climatic drivers to refine yield prediction models and improve agricultural decision-making under increasing climate variability. 4. Conclusions A key insight from this study is that relying on a single climate index as a definitive predictor of rainfall and temperature variability is not advisable, as its influence on maize yield is neither uniform nor universally applicable across all regions and growing seasons. While ENSO remains a well-established driver of interannual precipitation patterns, our results demonstrate that its effects—along with those of IOD, AAO, and TSA—exhibit significant spatial and temporal variability. This highlights the need to refine predictive models by incorporating multiple indices to better capture the complexity of climate impacts on yield variability. Integrating these indices into yield forecasting models enhances their accuracy and usability, providing valuable insights for decision-making in resource allocation, risk management, and agricultural planning. Future research should explore the synergistic effects of climate indices alongside agronomic and environmental factors, such as soil moisture availability, to further improve model precision. From an applied perspective, our findings have practical implications for agricultural management and climate adaptation strategies. Since maize yield data in this study are county-level averages, they do not capture fine-scale soil heterogeneity, topography, or prior management practices. However, they effectively represent regional trends, making the identified climate indices valuable decision-support tools. Given that these indices are publicly available in near real-time, they can be integrated into planting decisions (e.g., genotype selection, sowing date, planting density) and crop management practices (e.g., fertilization, irrigation scheduling). At the simplest level, the most-correlated index and its best-performing month (Fig. 6 ) could serve as a practical reference tool for optimizing production strategies. This study bridges the gap between large-scale climate variability and localized agricultural outcomes, offering a more nuanced understanding of how climate indices influence maize productivity. By improving our ability to anticipate climatic fluctuations, these findings contribute to the development of more resilient, adaptive agricultural systems in Argentina and beyond, supporting sustainable production strategies under increasing climate variability. Declarations Author Contribution María E. Fernández-Long: Methodology design, data validation, data analysis, and manuscript writing.Pablo Baldassini: Data processing and analysis, model development, figure preparation, and manuscript review.Marcos Texeira: Data curation, result interpretation, and figure preparation.Roberto J. Fernández: General supervision of the study, methodological guidance, and final manuscript review.Carlos M. Di Bella: Conceptualization, funding acquisition, supervision of the entire process, data analysis, manuscript writing, and critical review. Acknowledgements The authors would like to express their special gratitude for the funding provided by the PICT-2020-SERIEA-02102 project ( Regional Agricultural Production Estimation and Monitoring Model ) and the ANII-CONICET IA_2021_1_04 project ( Intelligent Management of Natural Resources - MIReN ). DATA AVAILABILITY No, I do not have any research data outside the submitted manuscript file. References Andreoli RV, Kayano MT (2006) Tropical Pacific and South Atlantic effects on rainfall variability over northeastern Brazil. Int J Climatol 26:1895–1912 Ashok K, Guan Z, Yamagata T (2001) Impact of the Indian Ocean dipole on the relationship between the Indian monsoon rainfall and ENSO. 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Eur J Agron 98:65–81 NOAA-CLPC (2024) : https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt , https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/aao/aao.shtml NOAA-PSL (2024) : https://psl.noaa.gov/data/correlation/tsa.data Otegui ME, Riglos M, Mercau JL (2021) Genetically modified maize hybrids and delayed sowing reduced drought effects across a rainfall gradient in temperate Argentina. J Exp Bot 72(14):5180–5188 Podesta G, Letson D, Messina C, Royce F, Ferreyra RA, Jones J, O'Brien JJ (2002) Use of ENSO-related climate information in agricultural decision making in Argentina: a pilot experience. Agric Syst 74(3):371–392 Pol M, Binyamin J (2014) Impact of climate change and variability on wheat and corn production in Buenos Aires, Argentina. American Journal of Climate Change, 2014 Quinn GP, Keough MJ (2002) Experimantal design and data analysis for biologists. Cambridge University Press Ray DK, Gerber JS, MacDonald GK, West PC (2015) Climate variation explains a third of global crop yield variability. Nat Commun 6(1):5989 Reboita MS, Ambrizzi T, Crespo NM, Dutra LMM, Ferreira GWdS, Rehbein A, Drumond A, da Rocha RP, Souza CA (2021) d. Impacts of teleconnection patterns on South America climate. Ann. N.Y. Acad. Sci., 1504: 116–153 Rotilli DH, Giorno A, y Maddonni GA (2019) Expansion of maize production in a semiarid region of Argentina: climatic and edaphic constraints and their implications on crop management. Agricultural Water Manage 225:105761 Rubio G, Lavado RS, Pereyra FX (eds) (2019) The soils of Argentina. Springer International Publishing, Madison, USA Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401(6751):360–363 Santos CAG, dos Santos DC, Neto RMB, de Oliveira G, dos Santos CAC, da Silva RM (2024) Analyzing the impact of ocean-atmosphere teleconnections on rainfall variability in the Brazilian Legal Amazon via the Rainfall Anomaly Index (RAI). Atmos Res. 307 Silvestri GE, Vera CS (2003) Antarctic Oscillation signal on precipitation anomalies over southeastern South America. Geophys Res Lett, 30(21) Spennemann PC, Naumann G, Peretti M, Cammalleri C, Salvia M, Bocco A, Long MEF, Lakshmi V (2024) Evaluation of a combined drought indicator against crop yield estimations and simulations over the Argentine Humid Pampas. Theoret Appl Climatol 155(8):7463–7478 Thompson DW, Wallace JM (2000) Annular modes in the extratropical circulation. Part I: Month-to-month variability. J Clim 13(5):1000–1016 Trenberth KE (1997) The definition of el niño. Bull Am Meteorol Soc 78(12):2771–2778 Trenberth KE, Dai A, Van Der Schrier G, Jones PD, Barichivich J, Briffa KR, Sheffield J (2014) Global warming and changes in drought. Nat Clim Change 4(1):17–22 Venegas SA, Mysak LA, Straub DN (1997) Atmosphere–ocean coupled variability in the South Atlantic. J Clim 10(11):2904–2920 Zebiak SE, Cane MA (1987) A model el niñ–southern oscillation. Mon Weather Rev 115(10):2262–2278 Zhang Z, Yang X, Liu Z, Bai F, Sun S, Nie J, Li S (2020) Spatio-temporal characteristics of agro-climatic indices and extreme weather events during the growing season for summer maize (Zea mays L.) in Huanghuaihai region, China. Int J Biometeorol 64:827–839 Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialsTable1.docx Cite Share Download PDF Status: Published Journal Publication published 17 Oct, 2025 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 05 May, 2025 Reviews received at journal 02 May, 2025 Reviews received at journal 25 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 31 Mar, 2025 Editor assigned by journal 24 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 20 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6269402","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444935895,"identity":"dee71169-d51d-4d91-9d6d-d8dfce0428ac","order_by":0,"name":"Pablo Baldassini","email":"","orcid":"","institution":"Universidad de Buenos Aires (UBA)","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Baldassini","suffix":""},{"id":444935896,"identity":"8ae2ffae-0562-43f8-874e-1ea3cf75b829","order_by":1,"name":"María E. Fernández-Long","email":"","orcid":"","institution":"Universidad de Buenos Aires (UBA)","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"E.","lastName":"Fernández-Long","suffix":""},{"id":444935897,"identity":"c524524b-fade-40c3-ad7b-e4c2ea5e90fc","order_by":2,"name":"Marcos Texeira","email":"","orcid":"","institution":"Universidad de Buenos Aires (UBA)","correspondingAuthor":false,"prefix":"","firstName":"Marcos","middleName":"","lastName":"Texeira","suffix":""},{"id":444935898,"identity":"93ac4962-25aa-4784-86bc-c8daa3efebd6","order_by":3,"name":"Roberto J. Fernández","email":"","orcid":"","institution":"Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA) – CONICET/UBA, CABA","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"J.","lastName":"Fernández","suffix":""},{"id":444935899,"identity":"56c882de-abe4-4d6a-9b29-cd6096ac2f53","order_by":4,"name":"Carlos M. Bella","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDCCwzwMDA8KGBj4ofwE4rQkGDAwSDYQreUAVIvBAWK18B3nPfggwcBO3vj48WsfPjAczuNvYH72AZ8WycN8yQYJBsmG287kFM+cwXC4WOIAm/EMfFoMDvOYSSQYMCeYHchJZuZhOJzYcIDBGK/DgFrMfyQY1CcY97+BaJl/gP0zIS1mQO8fTjCQSD8M1rLhAA9+W0B+ATrsuOGMG2+YGWcYpBcbHuYpxquF7/zZgx8+VFTL8/enP2b4UGGdJ3e8fTNeLUiABxQ7QJqZWA0MDOwPiFc7CkbBKBgFIwoAAHIVSHNcmIi8AAAAAElFTkSuQmCC","orcid":"","institution":"Universidad de Buenos Aires (UBA)","correspondingAuthor":true,"prefix":"","firstName":"Carlos","middleName":"M.","lastName":"Bella","suffix":""}],"badges":[],"createdAt":"2025-03-20 11:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6269402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6269402/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-025-05813-4","type":"published","date":"2025-10-17T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81032559,"identity":"89b8b630-669c-4b5f-8c16-25a9965d9bc4","added_by":"auto","created_at":"2025-04-21 11:34:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":377284,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area corresponding to the selected counties from Argentina (n=171) where corn production is focused. The counties are distributed in 12 regions within different provinces: northwest of Buenos Aires province (n=16), northeast of Buenos Aires province (n=29), southwest of Buenos Aires province (n=12), southeast of Buenos Aires province (n=19), Chaco province (n=18), Córdoba province (n=19), Entre Ríos province (n=15), Jujuy province (n=3), La Pampa province (n=13), Misiones province (n=7), Salta province (n=6) and Santa Fe province (n=14), covering a total of 71 million hectares.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6269402/v1/a933b64e0635dba64d6f3205.png"},{"id":81033122,"identity":"5c30072c-06e2-48a1-a1e9-6777469d3910","added_by":"auto","created_at":"2025-04-21 11:42:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":691770,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between maize yield anomalies and ONI (Ocean Niño Index, related to ENSO). From left to right, maximum correlation recorded from June to March (A), month where the maximum correlation value was identified (B), and number of months in the period with significant correlation (C). In the maximum correlation map, colors indicate significance at different confidence levels. Lighter colors indicate a 90% confidence level, while darker colors indicate 95%, 99%, and 99.9% confidence levels, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6269402/v1/bf49faf913abd92d75fb30d8.png"},{"id":81032567,"identity":"31269990-2b40-42b6-aff9-77ecd1b36185","added_by":"auto","created_at":"2025-04-21 11:34:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":524026,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between maize yield anomalies and IOD (INDEAN OCEAN DIPOLE index). From left to right, maximum correlation recorded from June to March (A), month where the maximum correlation value was identified (B), and number of months in the period with significant correlation (C). In the maximum correlation map, colors indicate significance at different confidence levels. Lighter colors indicate a 90% confidence level, while darker colors indicate 95%, 99%, and 99.9% confidence levels, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6269402/v1/60e32f29c16aea20615ee0c5.png"},{"id":81032564,"identity":"444bd5c2-1c70-4526-b1c1-6b024dfef76b","added_by":"auto","created_at":"2025-04-21 11:34:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":554443,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between maize yield anomalies and AAO (ANTARCTIC OSCILLATION index). From left to right, maximum correlation recorded from June to March (A), month where the maximum correlation value was identified (B), and number of months in the period with significant correlation (C). In the maximum correlation map, colors indicate significance at different confidence levels. Lighter colors indicate a 90% confidence level, while darker colors indicate 95%, 99%, and 99.9% confidence levels, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6269402/v1/ed90560f90309f72cd6b10db.png"},{"id":81033124,"identity":"db59e1df-2fe1-4785-a423-b6f5eb0a3643","added_by":"auto","created_at":"2025-04-21 11:42:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":543795,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between maize yield anomalies and TSA (SOUTH ATLANTIC OCEAN ANOMALY index). From left to right, maximum correlation recorded from June to March (A), month where the maximum correlation value was identified (B), and number of months in the period with significant correlation (C). In the maximum correlation map, colors indicate significance at different confidence levels. Lighter colors indicate a 90% confidence level, while darker colors indicate 95%, 99%, and 99.9% confidence levels, respectively.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6269402/v1/b3f4d2169a312999d2f0ef7b.png"},{"id":81032562,"identity":"f71829ec-17ca-40c1-841f-c5e2bc915b86","added_by":"auto","created_at":"2025-04-21 11:34:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":462720,"visible":true,"origin":"","legend":"\u003cp\u003eFrom left to right and from top to bottom, the correlation value for the Indian Ocean Dipole (IOD), Antarctic Oscillation index (AAO), South Atlantic Ocean Anomaly (TSA) and Ocean Niño Index (ONI) indices for the months of December (Dec), January (Jan), June (Jun), and January (Jan), respectively.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6269402/v1/1a2705dd3fb7cc2b1b6f4dbb.png"},{"id":81033367,"identity":"01d979f9-e1f3-4e3c-aa6f-484d01ffb583","added_by":"auto","created_at":"2025-04-21 11:50:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":240308,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis biplot. The first axis captures 46.52 % of total variability and the second one captures 17.06%. Point colors depict the counties region. Arrows represent the variables included in the component construction, namely the climatic indices and the corn yield anomalies.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6269402/v1/0fb1003bae830926249682c4.png"},{"id":93956125,"identity":"684b2bd9-40d9-4e76-a1de-4e79059e64d1","added_by":"auto","created_at":"2025-10-20 16:10:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3805548,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6269402/v1/5ed4c8f2-0e4d-4b46-82db-a877ad829d44.pdf"},{"id":81032572,"identity":"bc83dbed-67d4-4027-a3f3-afee0741e7e0","added_by":"auto","created_at":"2025-04-21 11:34:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":208318,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialsTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6269402/v1/420567c3b0d367840f623f7f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate variability and corn yield in Argentina: the role of El Niño–Southern Oscillation index and other climate drivers","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eONI is the dominant predictor, particularly in the core production region of the country.\u003c/li\u003e\n \u003cli\u003eIOD also exerts a significant influence, although it is more localized and seasonal.\u003c/li\u003e\n \u003cli\u003eThe effects of AAO and TSA are weaker and even more regionally constrained.\u003c/li\u003e\n \u003cli\u003eThe results underline the necessity of multi-index approaches for enhance yield forecasting accuracy.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eIn Argentina, corn yields are significantly influenced by diverse climatic interactions across varying agro-ecological zones (e.g. Ray et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Casali et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rainfall is essential for corn production; insufficient water during critical growth stages such as flowering and grain filling can lead to severe yield reductions, while excessive rainfall can result in waterlogging, heightened disease risk, and root damage (Lobell \u0026amp; Field, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lobell \u0026amp; Gourdji, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Another driver is temperature: moderate warm weather can stimulate growth, but warmer temperatures can shorten the crop cycle, and extreme heat during flowering disrupts reproduction, leading to significant losses of yield (Deryng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Equally important is soil moisture - largely influenced by rainfall, evapotranspiration, texture and soil depth (Trenberth et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Together, these factors underscore the need for more precise climate forecasting, enabling adaptive strategies that strengthen productivity and resilience under changing climate conditions, particularly in a context where an increase in drought frequency, severity, and extreme temperatures is projected over southern South America (Spennemann et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDifferent modes of variability have been identified as tools to simplify the understanding of the climate system by distilling its complexity into observable and predictable patterns (Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In turn, climate indices condense complex atmospheric and oceanic interactions into patterns that can be associated with climatic shifts over time. Although these indices do not provide direct forecasts of variables like rainfall or temperature, they reveal broader atmospheric patterns that correlate with these drivers, offering stable and interpretable signals for long-term climate assessments (Zebiak \u0026amp; Cane, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). By capturing large-scale atmospheric dynamics, climate indices provide valuable insights for rainfed agricultural systems, where regional and seasonal climatic variations largely determine yield outcomes (Feng et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Karimzadeh Soureshjani, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Integrating these indices into yield prediction models allows to understand the influence of climatic drivers on crop performance over time and space, underscoring their critical role in supporting agriculture under increasingly variable climatic conditions (Lalić et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mathieu \u0026amp; Aires, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalyzing multiple climate indices over time and space has allowed to better understand the complex interactions that drive corn yield variability all around the world (e.g. Meyer et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Jozami et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The use of indices that quantify the ENSO phenomenon (El Ni\u0026ntilde;o-Southern Oscillation), such as ONI (Ocean Ni\u0026ntilde;o Index, Trenberth \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), and IOD (Indian Ocean Dipole, Saji et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), TSA (Tropical Southern Atlantic Index, Enfield et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), or AAO (Antarctic Oscillation, Thompson \u0026amp; Wallace \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) has provided valuable insights into the temporal and regional effects on yield outcomes (Pol \u0026amp; Binyamin, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nguyen-Huy et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; de la Casa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). ENSO-induced rainfall anomalies, for example, have a direct impact on corn yields, particularly during critical growth phases (e.g. Gimms et al., 2000; Podest\u0026aacute; et al., 2002). IOD-induced precipitation changes are often context-dependent, varying according to local atmospheric dynamics and their interactions with other indices (Saji et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Ashok et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Similarly, TSA and AAO influence temperature and moisture distribution, affecting crop development (Venegas et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Silvestri \u0026amp; Vera, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). While indices have limitations --such as ENSO\u0026rsquo;s broad-scale influence and IOD\u0026rsquo;s inconsistent predictability\u0026ndash; their integration into predictive models has improved yield forecasts, enabling more informed decision-making (Iizumi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, this study aims to enhance the predictive understanding of key climate indices \u0026ndash; such us ONI, IOD, TSA, and AAO --for corn yield across Argentina\u0026rsquo;s diverse agro-ecological regions. We explored three critical questions regarding the predictive power of each index: 1) How accurately can this index forecast yield variability? 2) During which stages of the growing season are its predictions most reliable? and 3) In which regions of Argentina do each index demonstrate the greatest predictive strength? By integrating the forecasting capabilities of these indices, our analysis captured both temporal and spatial variability, accounting for regional differences and interannual fluctuations. Given Argentina\u0026rsquo;s vast and heterogeneous landscape, refining yield predictions can enable more effective management strategies, better resource allocation, and proactive risk mitigation.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. \u003cem\u003eStudy area\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eOur study focused on 171 counties (locally known as \u003cem\u003epartidos\u003c/em\u003e or \u003cem\u003edepartamentos)\u003c/em\u003e within Argentina\u0026rsquo;s primary corn-producing regions: northwest of Buenos Aires province (n\u0026thinsp;=\u0026thinsp;16), northeast of Buenos Aires province (n\u0026thinsp;=\u0026thinsp;29), southwest of Buenos Aires province (n\u0026thinsp;=\u0026thinsp;12), southeast of Buenos Aires province (n\u0026thinsp;=\u0026thinsp;19), Chaco province (n\u0026thinsp;=\u0026thinsp;18), C\u0026oacute;rdoba province (n\u0026thinsp;=\u0026thinsp;19), Entre Rios province (n\u0026thinsp;=\u0026thinsp;15), Jujuy province (n\u0026thinsp;=\u0026thinsp;3), La Pampa province (n\u0026thinsp;=\u0026thinsp;13), Misiones province (n\u0026thinsp;=\u0026thinsp;7), Salta province (n\u0026thinsp;=\u0026thinsp;6) and Santa Fe province (n\u0026thinsp;=\u0026thinsp;14) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These cover 71\u0026nbsp;million hectares, spanning from \u0026minus;\u0026thinsp;22\u0026deg; latitude in the north to -39\u0026deg; in the south, and from \u0026minus;\u0026thinsp;66\u0026deg; longitude in the west to -54\u0026deg; in the east, thus encompassing diverse climatic zones, soil types, topographies, and management practices (Bert et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Calvi\u0026ntilde;o \u0026amp; Monzon, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cabrini et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rubio et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; BAGE, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Corn (\u003cem\u003eZea mays L\u003c/em\u003e.) is one of the dominant crops in Argentina, with an average annual production of around 55\u0026nbsp;million tons, sown across over 10\u0026nbsp;million hectares each year (MAGyP \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The whole region features two distinct Southern-Hemisphere Spring and Summer sowing periods: early planting, typically occurring from late September to early October, and late planting, from December to early January (Maddonni, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Otegui et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Early planting is more common in the central and northern parts of the region, while late planting predominates towards the south and west (de Abelleyra et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), resulting in a varied exposure to climatic risks such as rainfall shortages, heat stress and excessive moisture during different growth stages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. \u003cem\u003eClimatological information\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo analyze the relationship between the spatio-temporal variability of climate and corn yields, we employed four key climate indices: ONI (Ocean Ni\u0026ntilde;o Index), TSA (Tropical Southern Anomaly), AAO (Antarctic Oscillation), and IOD (Indian Ocean Dipole) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These indices were extracted monthly for each county from July 1994 to May 2024, capturing interannual climate variability across the 30-yr study period. The ONI index, a widely recognized measure of ENSO variability, has been extensively used to study the impacts of El Ni\u0026ntilde;o events on regional climates (Trenberth, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Fern\u0026aacute;ndez Long et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jozami et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The TSA index, reflecting sea surface temperature anomalies in the South Atlantic, is crucial for understanding modulating rainfall patterns over South America (Andreoli \u0026amp; Kayano, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kayano et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Reboita et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Santos et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The AAO serves as an important indicator of atmospheric circulation in the Southern Hemisphere, reflecting both temperature and precipitation patterns (Gong \u0026amp; Wang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Thompson \u0026amp; Wallace, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Lastly, the IOD index provides insights into rainfall anomalies originating from the Indian Ocean, with downstream effects on South American climates (Saji et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClimate indices used and their data sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eiNDICES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDETAILS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLINK\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eONI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOceanic Ni\u0026ntilde;o Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOne of the most widely used ENSO indices. Calculated as the 3-month running mean of ERSST.v5 SST anomalies in the Ni\u0026ntilde;o 3.4 region (5\u0026deg;N\u0026ndash;5\u0026deg;S, 120\u0026deg;W\u0026ndash;170\u0026deg;W), based on centered 30-year base periods updated every 5 years, beginning in 1950.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt\u003c/span\u003e\u003cspan address=\"https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etsa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropical Southern Atlantic Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIt is an indicator of the surface temperatures in the Gulf of Guinea, the eastern tropical South Atlantic. Uses the anomaly of the average of the monthly SST from Eq-20S and 10E-30W from HadISST and NOAA OI 1x1 datasets. Climatology is? Spans from? 1971\u0026ndash;2000.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://psl.noaa.gov/data/correlation/tsa.data\u003c/span\u003e\u003cspan address=\"https://psl.noaa.gov/data/correlation/tsa.data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eiod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndian Ocean Dipole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProxy for a climate pattern affecting the Indian Ocean, calculated as the difference in sea surface temperature (SST) anomalies between two regions in the equatorial Indian Ocean: Western (50\u0026deg;E \u0026ndash; 70\u0026deg;E, 10\u0026deg;S \u0026ndash; 10\u0026deg;N) and Eastern (90\u0026deg;E \u0026ndash; 110\u0026deg;E, 10\u0026deg;S \u0026ndash; 0\u0026deg;). YEARS COVERED???\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sealevel.jpl.nasa.gov/data/vital-signs/indian-ocean-dipole/\u003c/span\u003e\u003cspan address=\"https://sealevel.jpl.nasa.gov/data/vital-signs/indian-ocean-dipole/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntarctic Oscillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe AAO or Southern Annular Mode (SAM) is the dominant pattern of natural variability in the Southern Hemisphere outside the tropics. Constructed by projecting the daily (00Z) 700-hPa height anomalies poleward of 20\u0026deg;S onto the leading pattern of the AAO. YEARS COVERED??\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/aao/aao.shtml\u003c/span\u003e\u003cspan address=\"https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/aao/aao.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. \u003cem\u003eCorn Production Information\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eCorn yield data were collected at the county level, covering the 1994/1995 to 2023/2024 growing seasons (Southern Hemisphere - Spring\u0026thinsp;+\u0026thinsp;Summer). The data were sourced from the official databases of the \u0026ldquo;\u003cem\u003eMinisterio de Agricultura, Ganader\u0026iacute;a y Pesca\u003c/em\u003e of Argentina\u0026rdquo; (MAGYP \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These are average county yields from rainfed, commercial (non-experimental) fields. To isolate the effects of climatic factors on yield variability, we applied a de-trending process to remove the influence of temporal trends linked to technological advancements, including genetic improvements, new machinery and chemicals, and enhanced management practices (Vossen 1989; Hough 1990). Previous work demonstrated that corn yields in Argentina showed a constant gain over the years (Gear, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rotilli et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Maddonni et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, linear time trends were calculated for each county. Then, the deviation of corn yields (MYD\u003csub\u003ei\u003c/sub\u003e) was calculated as the difference between the corn yield (MY) of a year (i) minus the linear trend (Trend\u003csub\u003ei\u003c/sub\u003e) for that year (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{MYD}_{i}={MY}_{i}-{Trend}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. \u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eClimate variability as captured by the four indices and corn yield data were analyzed both temporally and spatially using Spearman rank correlation (Quinn and Keough \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Similar methods have been widely employed in agricultural yield studies to establish climate-yield relationships (Ferreira et al. 2001; Ray et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fern\u0026aacute;ndez Long et al. 2025). The association and joint relationships among standardized climatic indices and corn yield anomalies (see above) were assessed by means of Principal Component Analysis (PCA, Legendre and Legendre \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and correlation biplot scaling of PCA ordination plots (Legendre and Legendre \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Borcard and others 2011). In this type of PCA scaling, the angles between variables or descriptors (the indices in different months and the corn yield anomalies.) reflect their correlations across the counties; angles from 0\u0026ordm; to 90\u0026ordm; represent decreasing positive correlations, whereas angles from 90\u0026ordm; to 180\u0026ordm; represent increasing negative ones. In turn, angles around 90\u0026ordm; represent uncorrelated descriptors.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eONI index, representing ENSO variability, exhibited significant positive detrended temporal correlations (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.10 to p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with corn yield anomalies in central Argentina, particularly for Northern Buenos Aires, Santa Fe, and Entre R\u0026iacute;os provinces. Correlation values ranged from 0.30 to 0.69 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A), predominantly throughout the productive cycle (June to March, i.e. winter through early fall) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B), with the strongest associations occurring for December-January (summer) central-east planting zone (Argentina Corn Belt), there coinciding with maximum vegetative growth and critical grain-filling stage, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA,B,C).This alignment underlines the importance of water availability for these rain-fed corn systems, as highlighted by Podest\u0026aacute; et al. (2002). However, in regions to the south and west such as La Pampa and southern Buenos Aires, correlations were weaker (r\u0026thinsp;\u0026lt;\u0026thinsp;0.50) and observed only in isolated counties without a clear temporal pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Materials Table\u0026nbsp;1). In turn, the northern provinces of Chaco, Salta, Jujuy, and Misiones showed no significant ENSO-related effects on corn yield. It is important to highlight that in the C\u0026oacute;rdoba province the strongest correlations occur for the spring months of September-October, in contrast to the rest of the core corn region (Buenos Aires, Santa Fe and Entre R\u0026iacute;os) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This pattern is very likely driven by the region\u0026rsquo;s soil moisture deficit, which is typically most pronounced in October (Fern\u0026aacute;ndez Long et al., 2020). Soil moisture availability at the beginning of the growing season is crucial for supporting early crop development, ultimately contributing to improved yield outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Indian Ocean Dipole (IOD) exhibited significant positive correlations (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 to p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in several regions of Argentina, particularly between December and March, aligning with corn's critical growth stages, including flowering and grain filling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B, C). However, the spatial distribution of correlations indicates that, while the IOD relates to rainfall patterns during this period, its impact is more localized and less extensive compared to that of the ONI. This suggests that the IOD reflects mid-season rainfall variability rather than being related with annual precipitation trends. Notably, the highest correlations were concentrated in central and eastern Argentina, whereas western and northern regions showed weaker or non-significant responses (with a few counties with negative ones). These findings align with previous studies highlighting the role of the IOD in modulating summer precipitation anomalies in South America, particularly through its interactions with other large-scale climatic drivers (Saji et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Cai et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Antarctic Oscillation index (AAO) exhibited a predominantly negative correlation with corn yields in Argentina, with significant figures (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 to p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) concentrated between November and January across the core corn-growing region, particularly in northern Buenos Aires and Entre R\u0026iacute;os (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B, C). For C\u0026oacute;rdoba, the most pronounced negative correlations were observed in January of year\u003csub\u003e+\u0026thinsp;1\u003c/sub\u003e, affecting most counties in the province. In contrast, other provinces showed no significant association between AAO variability and corn yield anomalies, reinforcing its more regionally specific and seasonally constrained impact. These findings align with previous studies indicating that while the AAO can modulate mid-latitude circulation patterns, its influence on rainfall anomalies in the Argentine Pampas is limited compared to stronger drivers such as ENSO or IOD (Thompson \u0026amp; Wallace, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Silvestri \u0026amp; Vera, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The mechanism behind this weaker influence likely stems from the AAO\u0026rsquo;s response to shifting the position and intensity of the subtropical jet stream, which primarily affects precipitation patterns in southern Argentina (outside our maps) rather than the central-north agricultural areas (Kidson, 1988; Gong \u0026amp; Wang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Although the AAO can influence the passage of cold fronts and transient weather systems, its effect on sustained precipitation anomalies critical for corn growth remains secondary to more dominant climatic drivers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe South Atlantic Ocean Anomaly (TSA) exhibited no significant correlation with corn yields across most of the analyzed regions in Argentina (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, C), with only a few localized exceptions. A weak but consistent negative effect was detected during the winter months for Entre R\u0026iacute;os and Santa Fe, while C\u0026oacute;rdoba and Buenos Aires (North and South) showed a moderate regional impact during the same period. This aligns with previous studies that indicate the TSA\u0026rsquo;s limited role in modulating climatic variability over subtropical South America, particularly during the corn-growing season (Venegas et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The TSA primarily affects sea surface temperature anomalies and atmospheric circulation over the South Atlantic, yet its downstream effects on precipitation and temperature anomalies in Argentina\u0026rsquo;s interior regions appear minimal when compared to more dominant climate drivers such as ENSO or IOD. One possible explanation for this negligible impact is the TSA\u0026rsquo;s weaker ability to generate persistent atmospheric anomalies that extend into the continent (Robertson \u0026amp; Mechoso, 2000; Bombardi \u0026amp; Carvalho, 2009). While the TSA can influence moisture advection from the Atlantic, its effect on regional precipitation is often masked by larger-scale atmospheric patterns. This would explain the TSA SOMEWHAT more significant influence on coastal rainfall patterns, with diminishing effects further inland, making it a less relevant predictor for corn yield variability in Argentina\u0026rsquo;s core agricultural areas. Consequently, when designing / building predictive models for corn yields, reliance on TSA alone may introduce unnecessary noise, which illustrates the importance of prioritizing indices with stronger and more consistent influences on the local climate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn sum, the ONI index exhibited the strongest correlations across the largest number of analyzed counties, reinforcing ENSO\u0026rsquo;s well-documented influence on interannual precipitation variability in southeastern South America (Boulanger et al., 2005; Barros et al., 2008; Cavalcanti et al., 2015; Andreoli et al., 2016; Iacovone et al., 2020). The highest ONI correlations were observed for January, a period that coincides with corn's critical reproductive phase, when water availability, also influenced by December precipitation is most crucial for yield determination (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, despite ENSO\u0026rsquo;s prominent influence, it does not fully explain rainfall variability, as inferred from crop yield anomalies. The Indian Ocean Dipole (IOD) also plays a significant role, particularly in December, when its positive phase enhances precipitation in southeastern South America, while its negative phase is linked to drought conditions, consistent with our findings (Gonzalez \u0026amp; Vera, 2010; Chan et al., 2008; Sena \u0026amp; Magnusdottir, 2021). In contrast, the Antarctic Oscillation (AAO) showed its strongest correlations in November, aligning with previous studies that identified its influence on precipitation patterns during late spring (Silvestri \u0026amp; Vera, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Negative AAO phases have been linked to positive precipitation anomalies in the region (Vera \u0026amp; Osman, 2018), further emphasizing the importance of this index in shaping early-season water availability. Meanwhile, the South Atlantic Ocean Anomaly (TSA) displayed its most significant correlations in June, suggesting a potential influence on pre-season moisture conditions, although its overall effect remained weaker compared to both ENSO and IOD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA principal component analysis (PCA) was performed to explore the complex interactions between climate indices (ENSO, IOD, AAO, and TSA) and maize yield anomalies, aiming to identify dominant patterns and joint drivers across Argentina's agro-ecological regions. The first two principal components (PC1 and PC2) together explained 63.58% of the total variance, with PC1 accounting for 46.52%, predominantly driven by ONI (winter and spring) and IOD (summer), reinforcing their critical role in shaping precipitation patterns and potentially temperature fluctuations during key growth phases such as flowering and grain filling. Meanwhile, PC2 (17.06%) was mainly associated with negative correlations of AAO and TSA, particularly in late spring and early summer, indicating their more localized and limited influence on yield variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The placement of maize yield anomalies in the biplot, positioned near ONI and IOD, highlights their stronger and more consistent impact on interannual yield fluctuations, while the weaker contributions of AAO and TSA suggest that their effects are either more regional or modulated by seasonal variability. Notably, the lack of clear spatial differentiation in PCA results suggests that maize yield responses to these indices are broadly consistent across Argentina, despite significant ecological diversity. These findings underscore the importance of a multi-index approach, integrating various climatic drivers to refine yield prediction models and improve agricultural decision-making under increasing climate variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eA key insight from this study is that relying on a single climate index as a definitive predictor of rainfall and temperature variability is not advisable, as its influence on maize yield is neither uniform nor universally applicable across all regions and growing seasons. While ENSO remains a well-established driver of interannual precipitation patterns, our results demonstrate that its effects\u0026mdash;along with those of IOD, AAO, and TSA\u0026mdash;exhibit significant spatial and temporal variability. This highlights the need to refine predictive models by incorporating multiple indices to better capture the complexity of climate impacts on yield variability. Integrating these indices into yield forecasting models enhances their accuracy and usability, providing valuable insights for decision-making in resource allocation, risk management, and agricultural planning. Future research should explore the synergistic effects of climate indices alongside agronomic and environmental factors, such as soil moisture availability, to further improve model precision.\u003c/p\u003e \u003cp\u003eFrom an applied perspective, our findings have practical implications for agricultural management and climate adaptation strategies. Since maize yield data in this study are county-level averages, they do not capture fine-scale soil heterogeneity, topography, or prior management practices. However, they effectively represent regional trends, making the identified climate indices valuable decision-support tools. Given that these indices are publicly available in near real-time, they can be integrated into planting decisions (e.g., genotype selection, sowing date, planting density) and crop management practices (e.g., fertilization, irrigation scheduling). At the simplest level, the most-correlated index and its best-performing month (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) could serve as a practical reference tool for optimizing production strategies.\u003c/p\u003e \u003cp\u003eThis study bridges the gap between large-scale climate variability and localized agricultural outcomes, offering a more nuanced understanding of how climate indices influence maize productivity. By improving our ability to anticipate climatic fluctuations, these findings contribute to the development of more resilient, adaptive agricultural systems in Argentina and beyond, supporting sustainable production strategies under increasing climate variability.\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eMar\u0026iacute;a E. Fern\u0026aacute;ndez-Long: Methodology design, data validation, data analysis, and manuscript writing.Pablo Baldassini: Data processing and analysis, model development, figure preparation, and manuscript review.Marcos Texeira: Data curation, result interpretation, and figure preparation.Roberto J. Fern\u0026aacute;ndez: General supervision of the study, methodological guidance, and final manuscript review.Carlos M. Di Bella: Conceptualization, funding acquisition, supervision of the entire process, data analysis, manuscript writing, and critical review.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to express their special gratitude for the funding provided by the PICT-2020-SERIEA-02102 project (\u003cem\u003eRegional Agricultural Production Estimation and Monitoring Model\u003c/em\u003e) and the ANII-CONICET IA_2021_1_04 project (\u003cem\u003eIntelligent Management of Natural Resources - MIReN\u003c/em\u003e).\u003c/p\u003e\n\u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e\n\u003cp\u003eNo, I do not have any research data outside the submitted manuscript file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndreoli RV, Kayano MT (2006) Tropical Pacific and South Atlantic effects on rainfall variability over northeastern Brazil. 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Int J Biometeorol 64:827\u0026ndash;839\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Yield forecasting, Teleconnections, Rainfed agriculture","lastPublishedDoi":"10.21203/rs.3.rs-6269402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6269402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding how climate variability influences corn yield is essential for improving agricultural forecasting and resilience strategies. This study examines the role of four major climate indices\u0026mdash;El Ni\u0026ntilde;o Southern Oscillation (ENSO) index related ONI, IOD, AAO and TSA\u0026mdash;on corn yield variability across Argentina's diverse agro-ecological regions (22\u0026deg; to 39\u0026deg; S latitude). Using historical rainfed yield data at the county level and monthly climate records (1994\u0026ndash;2024), we assessed the temporal and spatial relationships between these indices and detrended yield anomalies. ONI emerged as the dominant predictor, particularly in the core production region of the country, where its positive phase correlated strongly with increased rainfall during critical crop growth stages. IOD also exerted a significant influence, although it was more localized and seasonally dependent, primarily affecting mid-summer precipitation patterns at the core region. Conversely, the effects of AAO and TSA were weaker and even more regionally constrained (for Cordoba and coastal areas, respectively). A Principal Component Analysis (PCA) including all four indexes reinforced these findings, highlighting the combined and overlapping influence of climate indices rather than simple relationships. Notably, the absence of a clear regional differentiation in the PCA suggests that climate signals act broadly across Argentina, making it difficult to generalize yield predictions based on any single index. These results challenge the conventional assumption that ENSO alone can reliably forecast yield outcomes and underline the necessity of multi-index approaches for robust agricultural modeling. By integrating climate indices into predictive frameworks, this research could enhance yield forecasting accuracy, supporting adaptive management strategies in the face of increasing climate variability.\u003c/p\u003e","manuscriptTitle":"Climate variability and corn yield in Argentina: the role of El Niño–Southern Oscillation index and other climate drivers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 11:34:03","doi":"10.21203/rs.3.rs-6269402/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-05T12:36:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-02T18:41:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-25T17:03:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292313970414790471225166732891296828830","date":"2025-04-18T17:50:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256428291366936461898114753291482803412","date":"2025-04-03T13:33:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-31T14:42:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-24T06:53:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-24T06:50:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2025-03-20T11:50:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eb2fb29c-30de-4643-bbb1-5702aeb26aab","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:05:31+00:00","versionOfRecord":{"articleIdentity":"rs-6269402","link":"https://doi.org/10.1007/s00704-025-05813-4","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2025-10-17 15:57:24","publishedOnDateReadable":"October 17th, 2025"},"versionCreatedAt":"2025-04-21 11:34:03","video":"","vorDoi":"10.1007/s00704-025-05813-4","vorDoiUrl":"https://doi.org/10.1007/s00704-025-05813-4","workflowStages":[]},"version":"v1","identity":"rs-6269402","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6269402","identity":"rs-6269402","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}