Model-Assisted Optimization of Nitrogen Fertilizer Rates for Maize in Dry Zone of Sri Lanka under Current and Future Climate Scenarios

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Bandara, S.H.N.P. De Silva, W.C.P. Egodawatta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8737594/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Maize ( Zea mays L.) is the most vital upland cereal and a cornerstone of food security in Sri Lanka. The crop is extensively cultivated in the dry zone, a region highly vulnerable to climate change. Nevertheless, current nitrogen (N) fertilizer rates have not been recently climate-optimized, reducing yield efficiency and increasing environmental impacts. The objectives of this study were to model and optimize N fertilizer rates for maize, under current and future climate conditions for 2050 and 2100 by testing different nitrogen management scenarios by utilizing the Agricultural Production Systems Simulator (APSIM). Simulations were conducted using 30 years of past climate data from Mahailluppallama local weather station and 2023 IPCC climate projections to test multiple nitrogen management strategies. Results showed that the current Department of Agriculture recommendation of 315-425 kg ha⁻¹ N can be optimized to 345-435 kg ha⁻¹ under present conditions, increasing yields while reducing leaching losses. Under future climates, optimal N rates rose slightly to 375-465 kg ha⁻¹ to maintain productivity, though leaching risks increased. Findings highlight the importance of adaptive, climate-responsive N management strategies to sustain maize yield and minimize environmental impacts. The outcomes provide valuable guidance for developing data-driven, climate-smart fertilizer recommendations for dry zone of the Sri Lanka. Climate Analysis and Modeling Agronomy Agricultural Engineering Horticulture APSIM maize model climate change climate-specific optimization leaching nitrogen management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Maize ( Zea mays L.) is the second-most important cereal crop cultivated in Sri Lanka. Farmers favor hybrid varieties, and in recent years, the cultivation of hybrid crops has expanded due to their high production potential (Rathnayake & Dharmawardhana, 2025 ). Maize is predominantly regarded as a crop suited to the dry zone. It is cultivated both as a monoculture and as part of mixed cropping systems in settled highland areas. Nitrogen is the most yield-limiting nutrient for maize, as its deficiency delays plant growth and development, thereby reducing grain yield (Ngala et al., 2023 ). Therefore, to achieve high maize yields, nitrogen fertilization and management techniques remain important agronomic procedures(Lakmali et al., 2024 ). Careful management of nitrogen fertilizer is essential to mitigate environmental risks like soil degradation and water pollution (Bijay-Singh & Craswell, 2021a ), while also ensuring that farmers see a substantial return on their investment before purchasing fertilizers. The twenty-first century has seen climate change emerge as a critical environmental concern, with agriculture being one of the most vulnerable sectors. As climatic conditions change, it becomes imperative to adjust agricultural practices to ensure food security and sustainable farming (Samarasinghe et al., 2025 ). Agricultural systems models, such as the Agricultural Production Systems Simulator (APSIM), are increasingly used worldwide to explore strategies that address food security, optimize resource use, and adapt to climate change impacts (Rusinamhodzi et al., 2025 ). APSIM, in particular, is a powerful tool for simulating crop-soil-climate interactions and guiding decisions on agronomic practices, such as optimizing nitrogen fertilizer applications. This capability is particularly relevant for dry zone of Sri Lanka, where maize production is sensitive to both nutrient management and changing climatic conditions (Pasley et al., 2023 ). APSIM allows for the evaluation of different management practices, including nitrogen fertilizer application, under both current and projected climate scenarios. For this study, an APSIM model that has been previously calibrated and validated for maize cultivation under similar environmental conditions were utilized (Amarasingha et al., 2017 ). This previously calibrated model provides a reliable foundation for accurately simulating maize growth in the dry zone of Sri Lanka. By building on this established model, the research focused on optimizing nitrogen fertilizer rates and projecting these rates under future climate scenarios. This approach helped to identify sustainable practices that can enhance maize productivity while mitigating environmental impacts. Even though maize is a key crop for food security and smallholder livelihoods in Sri Lanka’s dry zone, but productivity is limited by inefficient nitrogen (N) management. Farmers lack precise guidance on optimal N rates, leading to under-application, which reduces yields, or over-application, causing economic losses, environmental damage, and increased greenhouse gas emissions (Bijay-Singh & Craswell, 2021b ). Existing fertilizer recommendations from the Department of Agriculture are outdated and do not reflect recent changes in climate and farming practices. Increasingly unpredictable weather, rising temperatures, altered rainfall patterns, and extreme events further complicate N management, highlighting the need for updated, climate-responsive fertilizer guidelines. This study aimed to develop a nitrogen (N) fertilizer optimization model for maize cultivation in Sri Lanka’s dry zone using APSIM, which had been previously calibrated for comparable environmental conditions. By simulating a range of N application scenarios, the research identified optimal fertilizer requirements under future climate projections, offering strategies to improve maize yields while minimizing the environmental impact of nitrogen use. The study also generated evidence-based recommendations for policymakers and extension services to support the development of updated, sustainability-oriented fertilizer guidelines. Overall, the findings contribute to enhancing the resilience of maize farming in the dry zone, helping farmers adapt more effectively to the challenges posed by climate change. Materials and Methods Study area and data collection The research focused on maize cultivation in the dry zone of Sri Lanka, specifically using daily weather data, including maximum (T MAX ) and minimum (T MIN ) temperatures, rainfall, and sunshine hours from the weather station at Field Crop Research and Development Institute (FCRDI) at Mahailuppallama. Soil characteristics for the study area were obtained from (Mapa, 2010 ). Soil data for individual layers were incorporated into the model when available, while default model values were applied for the deeper subsoil layers. The collected data serves as the basis for simulating maize growth under various nitrogen management scenarios. Adjustments to key climate variables, including maximum temperature (T MAX ), minimum temperature (T MIN ), rainfall, and CO2 concentration, were made using projections from climate change models. The data was derived from the Representative Concentration Pathways (RCP) 8.5 scenario, which was outlined in the Intergovernmental Panel on Climate Change, 2023 synthesis report (Calvin et al., 2023 ). This scenario represents a high-emission pathway, reflecting the potential trajectory of climate change without significant mitigation efforts. (Table 1 ) Table 1 IPCC Future Climate Projections (RCP 8.5 Scenario) Climatic factor Scenario 2022 (Current) 2050 2100 Rainfall (Increased) 0 10% 20% T MAX (Increased) 0 1.7 3.6 T MIN (Increased) 0 1.7 3.6 CO 2 Concentration 415 ppm 550 ppm 940 ppm Simulation model (APSIM) APSIM, or the Agricultural Production Systems Simulator (version 7.10), is used to simulate maize growth under a range of nitrogen fertilizer rates. To ensure accurate simulation, a maize model previously calibrated and validated under similar environmental conditions was obtained from Amarasinghe et al. (2017) and used in this study. The study tested 784 different nitrogen fertilizer application scenarios, adjusting total rates of application from 0 kg ha − 1 to 810 kg ha − 1 . This included simulations for both the Yala and Maha growing seasons, allowing the research to cover the two primary planting seasons in Sri Lanka. All management practices were determined in accordance with the guidelines provided by the Department of Agriculture, Sri Lanka (2013). This involved identifying specific recommendations for planting dates, planting methods (such as direct seeding), and irrigation practices for Yala season in dry zone. These management practices were then integrated into the model simulations, ensuring that the simulated crop growth and development reflected real-world agricultural practices observed in dry zone, Sri Lanka. Results and Discussion Under the present climate scenario, maize yield increased proportionally with nitrogen application up to a threshold. The Department of Agriculture recommends a total nitrogen application of 315 kg ha − 1 in the Maha season (Fig. 1 ) and 425 kg ha − 1 in the Yala season (Fig. 2 ), which yielded 2597.67 kg ha − 1 and 3168.84 kg ha − 1 , respectively. Corresponding leaching rates were 14.80 kg ha − 1 for Maha and while it was only 0.55 kg ha − 1 for Yala , demonstrating lower leaching risks in drier conditions. Simulation results suggested that modest increases in total nitrogen inputs up to 345 kg N ha⁻¹ for Maha and 435 kg N ha⁻¹ for Yala could slightly improve yields up to 2625.10 kg ha⁻¹ in Maha and 3174.46 kg ha⁻¹ in Yala , while decreasing leaching losses in Maha to 11.3 kg ha⁻¹, and maintaining relatively constant minimal leaching level in Yala . This indicates potential to enhance nitrogen use efficiency for present climate conditions through optimized application rates, particularly in the Maha season. In 2050 climate scenario, nitrogen application for the Maha season remained the same as in the present evaluation at 345 kg ha − 1 , yielding 2650.03 kg ha − 1 , while leaching losses increased to 13.75 kg ha − 1 due to projected climate variability (Fig. 3 ). For the Yala season, total nitrogen remained 435 kg ha − 1 , with a yield increase to 3182.09 kg ha − 1 and leaching losses rising slightly to 0.65 kg ha − 1 (Fig. 4 ). The summary analysis of the 2050 scenario suggested that slightly higher yield than the present, however with a cost of increased nitrogen leaching in both seasons. By 2100, adjustments to nitrogen rates were necessary to account for projected climate impacts. In the Maha season, the total nitrogen applied increased to 375 kg ha − 1 , resulting in a yield of 2675.05 kg ha − 1 with leaching losses of 14.17 kg ha − 1 . Similarly, in the Yala season, the total nitrogen applied increased to 465 kg ha − 1 , producing a yield of 3198.57 kg ha − 1 , with leaching losses rising to 1.05 kg ha − 1 . 2100 scenario evaluations highlighted the increasing challenges of maintaining yields while mitigating environmental impacts under changing climatic conditions. The findings of this study emphasize the critical importance of adopting dynamic fertilizer recommendations tailored to seasonal and climatic variability. Site-specific nitrogen management, rather than generalized recommendations, is essential for optimizing yields and minimizing environmental losses. Optimized nitrogen fertilizer rates have been shown to significantly increase maize productivity while reducing reactive nitrogen losses, such as N₂O emissions, NH₃ volatilization, and nitrate leaching (Du et al., 2024 ). This aligns with findings by (Zhang et al., 2023 ), who demonstrated the significant economic benefits of nitrogen optimization in maize production. However, nitrogen optimization alone is insufficient to ensure sustainable maize production. Other critical factors such as water management, soil health, and climate-smart practices which play a pivotal role in enhancing productivity and resilience. Integrated approaches that combine split fertilizer applications (Ojeniyi et al., 2024 )), irrigation management (Zhang et al., 2020 ) and soil conservation techniques (Allart et al., 2023 ) are essential to improving nitrogen use efficiency and reducing environmental losses. This supports the argument by (Zhou, 2016 ) that holistic strategies are necessary for sustaining crop productivity under changing climates. The use of localized data for weather, soil, and crop parameters was a key strength of this study, as it enabled reliable predictions and actionable insights. These findings align with (Pasquel et al., 2023 ), who emphasized the importance of site-specific data for refining agricultural practices. Tailoring fertilizer rates to match local conditions and future climatic scenarios minimizes environmental risks, such as nitrogen leaching, which tends to increase with higher nitrogen application rates (Yao et al., 2021 ). Balanced fertilizer use, integrated with climate-resilient strategies, is therefore crucial to mitigating these risks while sustaining productivity. This research underscores the value of simulation modeling as a tool for developing location-specific recommendations. By integrating simulation outputs with on-farm practices, farmers and policymakers can enhance nitrogen use efficiency, reduce environmental losses, and sustain maize productivity under changing climatic conditions. Future studies should explore the integration of water management, soil conservation, and adaptive cropping systems to complement nitrogen optimization, addressing its limitations when used in isolation. From a policy perspective, dynamic fertilizer recommendations must be coupled with farmer education and capacity-building programs to promote the adoption of climate-smart practices (Bhanuwanti et al., 2024). Equipping farmers with knowledge and tools for integrated nutrient management, efficient water use, and soil health improvement is essential to fostering resilient agricultural systems that can cope with climate variability. Financial incentives, such as subsidies for high-quality seeds and fertilizers and access to affordable credit for smallholder farmers, can further enhance the economic feasibility of adopting these practices (Dulanjani & Shantha, 2022 ). These measures would ensure improved productivity, environmental sustainability, and long-term viability in the agricultural sector. Conclusion Adjustments in nitrogen application timing were suggested to align with altered rainfall patterns. Reduced basal fertilizer and increased nitrogen application four weeks after sowing improved nutrient uptake and NUE during critical growth stages. Despite these optimizations, nitrogen leaching increased under future scenarios, especially during the Maha season, due to higher rainfall intensity. The study emphasized the need for climate-resilient nitrogen management strategies that integrate water management, soil health, and climate-smart practices to enhance resilience and minimize environmental impacts. Future research should focus on optimizing nitrogen under water-limited conditions and refining recommendations using localized soil and climate data. In conclusion, the research provided a framework for adaptive nitrogen management that balances productivity and environmental sustainability, enabling farmers and policymakers to address climate challenges while ensuring long-term agricultural viability. References Allart K, Almoussawi A, Kerbey L, Catterou M, Roger D, Mortier D, Blanc E, Robert B, Spicher F, Emery L, Hirel B, Dubois F, Tetu T (2023) Splitting Nitrogen Fertilization Is More Important than Nitrogen Level When Mixed Wheat Varieties Are Cultivated in a Conservation Agriculture System. Agronomy 13(5):1295. https://doi.org/10.3390/agronomy13051295 Amarasingha RPRK, Suriyagoda LDB, Marambe B, Rathnayake WMUK, Gaydon DS, Galagedara LW, Punyawardena R, Silva GLLP, Nidumolu U, Howden M (2017) Improving water productivity in moisture-limited rice-based cropping systems through incorporation of maize and mungbean: A modelling approach. Agric Water Manage 189:111–122. https://doi.org/10.1016/j.agwat.2017.05.002 Bhanuwanti, Dar KA, Singh L, Saad AA, Rai U, Tanwar H, Khatoon A (2024) Climate Smart Agriculture: Innovating Sustainable Practices for a Changing Climate. J Sci Res Rep 30(11):568–576. https://doi.org/10.9734/jsrr/2024/v30i112585 Bijay-Singh, Craswell E (2021a) Fertilizers and nitrate pollution of surface and ground water: An increasingly pervasive global problem. SN Appl Sci 3(4):518. https://doi.org/10.1007/s42452-021-04521-8 Bijay-Singh, Craswell E (2021b) Fertilizers and nitrate pollution of surface and ground water: An increasingly pervasive global problem. SN Appl Sci 3(4):518. https://doi.org/10.1007/s42452-021-04521-8 Calvin K, Dasgupta D, Krinner G, Mukherji A, Thorne PW, Trisos C, Romero J, Aldunce P, Barrett K, Blanco G, Cheung WWL, Connors S, Denton F, Diongue-Niang A, Dodman D, Garschagen M, Geden O, Hayward B, Jones C, Ha M (2023) IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland. (P. Arias, M. Bustamante, I. Elgizouli, G. Flato, M. Howden, C. Méndez-Vallejo, J. J. Pereira, R. Pichs-Madruga, S. K. Rose, Y. Saheb, R. Sánchez Rodríguez, D. Ürge-Vorsatz, C. Xiao, N. Yassaa, J. Romero, J. Kim, E. F. Haites, Y. Jung, R. Stavins, … C. Péan, Eds.; First). Intergovernmental Panel on Climate Change (IPCC). https://doi.org/10.59327/IPCC/AR6-9789291691647 Du Y, Lu Y, Guo S, Wang R, Song X, Ju X (2024) Enhanced efficiency nitrogen fertilizers (EENFs) can reduce nitrous oxide emissions and maintain high grain yields in a rain-fed spring maize cropping system. Field Crops Res 312:109408. https://doi.org/10.1016/j.fcr.2024.109408 Dulanjani PA, Shantha AA (2022) The Impact of Fertilizer Subsidy on Average Paddy Yield in Sri Lanka. Sri Lanka J Social Sci Humanit 2(2):105–116. https://doi.org/10.4038/sljssh.v2i2.77 Lakmali AWS, Dissanayake DMD, Malaviarachchi MAPWK, Jayarathna SRGHSM (2024) Estimating the Plant Nutrient Uptake of Selected Maize Varieties in Sri Lanka’s Dry Zone at Various Fertilizer Rates. Asian J Res Agric Forestry 10(3):157–167. https://doi.org/10.9734/ajraf/2024/v10i3308 Mapa R (2010) Soils of the Dry Zone of Sri Lanka . Mapa, R.B., Somasiri S., Dassanayake, A.R. (2010). Soil of the Dry zone of Sri Lanka: Morphology, characterization and classification. 1st edition, Soil Science Society of Sri Lanka , Peradeniya Ngala AL, Kwari D, J., Makinta G (2023) Determination of Most Limiting Nutrient for Maize Yield through Nutrient Omission Experiment for Site-Specific Nutrient Recommendation on Typic ustipssament of Maiduguri, Northeast Nigeria By. Acta Sci Agric 7(8):25–32. https://doi.org/10.31080/ASAG.2023.07.1284 Ojeniyi K, Ngonidzashe C, Devkota K, Madukwe D (2024) Optimizing split-fertilizer applications for enhanced maize yield and nutrient use efficiency in Nigeria’s Middle-belt. Heliyon 10(19):e37747. https://doi.org/10.1016/j.heliyon.2024.e37747 Pasley H, Wenham K, Bell L, Huth N, Holzworth D, Chaki A, Gaydon D, Collins M (2023) APSIM next generation mungbean model: A tool for advancing mungbean production. Field Crops Res 298:108955. https://doi.org/10.1016/j.fcr.2023.108955 Pasquel D, Cammarano D, Roux S, Castrignanò A, Tisseyre B, Rinaldi M, Troccoli A, Taylor JA (2023) Downscaling the APSIM crop model for simulation at the within-field scale. Agric Syst 212:103773. https://doi.org/10.1016/j.agsy.2023.103773 Rathnayake RMDH, Dharmawardhana DTPS (2025) Adoption of Best Cultivation Practices and Post-Harvest Techniques among Maize Farmers in Anuradhapura District, Sri Lanka. J Multidisciplinary Translational Res 10(1):66–76. https://doi.org/10.4038/jmtr.v10i1.81 Rusinamhodzi L, Berre D, Thierfelder C, Ridaura S-L, Corbeels M (2025) Modelling the climate change adaptation potential of no-tillage maize systems in southern Africa. Mitig Adapt Strat Glob Change 30(5):36. https://doi.org/10.1007/s11027-025-10223-y Samarasinghe BKDJR, Zhu Y, Abeynayake NR, Zeng X, Mathavan B, Wanninayake RWWMPK, Rasheed S, Bah AS (2025) Climate change and rice production in Sri Lanka: Short-run vs. long-run symmetric and asymmetric effects. Front Sustainable Food Syst 9:1592542. https://doi.org/10.3389/fsufs.2025.1592542 Yao Z, Zhang W, Chen Y, Zhang W, Liu D, Gao X, Chen X (2021) Nitrogen leaching and grey water footprint affected by nitrogen fertilization rate in maize production: A case study of Southwest China. Journal of the Science of Food and Agriculture , 101 . https://doi.org/10.1002/jsfa.11263 Zhang L, Zhang W-S, Meng Q-F, Hu Y-C, Schmidhalter U, Zhong C-H, Zou G-Y, Chen X-P (2023) Optimizing Agronomic, Environmental, Health and Economic Performances in Summer Maize Production through Fertilizer Nitrogen Management Strategies. Plants 12(7):1490. https://doi.org/10.3390/plants12071490 Zhang Z, Zhang Y, Shi Y, Yu Z (2020) Optimized split nitrogen fertilizer increase photosynthesis, grain yield, nitrogen use efficiency and water use efficiency under water-saving irrigation. Sci Rep 10(1):20310. https://doi.org/10.1038/s41598-020-75388-9 Zhou M (2016) Sustaining crop productivity while reducing environmental nitrogen losses in the subtropical wheat-maize cropping systems: A comprehensive case study of nitrogen cycling and balance. Agriculture Ecosystems & Environment , 231 . https://doi.org/10.1016/j.agee.2016.06.022 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Farmers favor hybrid varieties, and in recent years, the cultivation of hybrid crops has expanded due to their high production potential (Rathnayake \u0026amp; Dharmawardhana, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Maize is predominantly regarded as a crop suited to the dry zone. It is cultivated both as a monoculture and as part of mixed cropping systems in settled highland areas.\u003c/p\u003e \u003cp\u003eNitrogen is the most yield-limiting nutrient for maize, as its deficiency delays plant growth and development, thereby reducing grain yield (Ngala et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, to achieve high maize yields, nitrogen fertilization and management techniques remain important agronomic procedures(Lakmali et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Careful management of nitrogen fertilizer is essential to mitigate environmental risks like soil degradation and water pollution (Bijay-Singh \u0026amp; Craswell, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e), while also ensuring that farmers see a substantial return on their investment before purchasing fertilizers.\u003c/p\u003e \u003cp\u003eThe twenty-first century has seen climate change emerge as a critical environmental concern, with agriculture being one of the most vulnerable sectors. As climatic conditions change, it becomes imperative to adjust agricultural practices to ensure food security and sustainable farming (Samarasinghe et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Agricultural systems models, such as the Agricultural Production Systems Simulator (APSIM), are increasingly used worldwide to explore strategies that address food security, optimize resource use, and adapt to climate change impacts (Rusinamhodzi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). APSIM, in particular, is a powerful tool for simulating crop-soil-climate interactions and guiding decisions on agronomic practices, such as optimizing nitrogen fertilizer applications. This capability is particularly relevant for dry zone of Sri Lanka, where maize production is sensitive to both nutrient management and changing climatic conditions (Pasley et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAPSIM allows for the evaluation of different management practices, including nitrogen fertilizer application, under both current and projected climate scenarios. For this study, an APSIM model that has been previously calibrated and validated for maize cultivation under similar environmental conditions were utilized (Amarasingha et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This previously calibrated model provides a reliable foundation for accurately simulating maize growth in the dry zone of Sri Lanka. By building on this established model, the research focused on optimizing nitrogen fertilizer rates and projecting these rates under future climate scenarios. This approach helped to identify sustainable practices that can enhance maize productivity while mitigating environmental impacts.\u003c/p\u003e \u003cp\u003eEven though maize is a key crop for food security and smallholder livelihoods in Sri Lanka\u0026rsquo;s dry zone, but productivity is limited by inefficient nitrogen (N) management. Farmers lack precise guidance on optimal N rates, leading to under-application, which reduces yields, or over-application, causing economic losses, environmental damage, and increased greenhouse gas emissions (Bijay-Singh \u0026amp; Craswell, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Existing fertilizer recommendations from the Department of Agriculture are outdated and do not reflect recent changes in climate and farming practices. Increasingly unpredictable weather, rising temperatures, altered rainfall patterns, and extreme events further complicate N management, highlighting the need for updated, climate-responsive fertilizer guidelines.\u003c/p\u003e \u003cp\u003eThis study aimed to develop a nitrogen (N) fertilizer optimization model for maize cultivation in Sri Lanka\u0026rsquo;s dry zone using APSIM, which had been previously calibrated for comparable environmental conditions. By simulating a range of N application scenarios, the research identified optimal fertilizer requirements under future climate projections, offering strategies to improve maize yields while minimizing the environmental impact of nitrogen use. The study also generated evidence-based recommendations for policymakers and extension services to support the development of updated, sustainability-oriented fertilizer guidelines. Overall, the findings contribute to enhancing the resilience of maize farming in the dry zone, helping farmers adapt more effectively to the challenges posed by climate change.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and data collection\u003c/h2\u003e \u003cp\u003eThe research focused on maize cultivation in the dry zone of Sri Lanka, specifically using daily weather data, including maximum (T\u003csub\u003eMAX\u003c/sub\u003e) and minimum (T\u003csub\u003eMIN\u003c/sub\u003e) temperatures, rainfall, and sunshine hours from the weather station at Field Crop Research and Development Institute (FCRDI) at Mahailuppallama. Soil characteristics for the study area were obtained from (Mapa, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Soil data for individual layers were incorporated into the model when available, while default model values were applied for the deeper subsoil layers. The collected data serves as the basis for simulating maize growth under various nitrogen management scenarios.\u003c/p\u003e \u003cp\u003eAdjustments to key climate variables, including maximum temperature (T\u003csub\u003eMAX\u003c/sub\u003e), minimum temperature (T\u003csub\u003eMIN\u003c/sub\u003e), rainfall, and CO2 concentration, were made using projections from climate change models. The data was derived from the Representative Concentration Pathways (RCP) 8.5 scenario, which was outlined in the Intergovernmental Panel on Climate Change, 2023 synthesis report (Calvin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This scenario represents a high-emission pathway, reflecting the potential trajectory of climate change without significant mitigation efforts. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eIPCC Future Climate Projections (RCP 8.5 Scenario)\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\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClimatic factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022 (Current)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2100\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall (Increased)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003eMAX\u003c/sub\u003e (Increased)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003eMIN\u003c/sub\u003e (Increased)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e Concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415 ppm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e550 ppm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e940 ppm\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\n\u003ch3\u003eSimulation model (APSIM)\u003c/h3\u003e\n\u003cp\u003eAPSIM, or the Agricultural Production Systems Simulator (version 7.10), is used to simulate maize growth under a range of nitrogen fertilizer rates. To ensure accurate simulation, a maize model previously calibrated and validated under similar environmental conditions was obtained from Amarasinghe et al. (2017) and used in this study. The study tested 784 different nitrogen fertilizer application scenarios, adjusting total rates of application from 0 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 810 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. This included simulations for both the \u003cem\u003eYala\u003c/em\u003e and \u003cem\u003eMaha\u003c/em\u003e growing seasons, allowing the research to cover the two primary planting seasons in Sri Lanka.\u003c/p\u003e \u003cp\u003eAll management practices were determined in accordance with the guidelines provided by the Department of Agriculture, Sri Lanka (2013). This involved identifying specific recommendations for planting dates, planting methods (such as direct seeding), and irrigation practices for \u003cem\u003eYala\u003c/em\u003e season in dry zone. These management practices were then integrated into the model simulations, ensuring that the simulated crop growth and development reflected real-world agricultural practices observed in dry zone, Sri Lanka.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003eUnder the present climate scenario, maize yield increased proportionally with nitrogen application up to a threshold. The Department of Agriculture recommends a total nitrogen application of 315 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the \u003cem\u003eMaha\u003c/em\u003e season (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and 425 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the \u003cem\u003eYala\u003c/em\u003e season (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which yielded 2597.67 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 3168.84 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Corresponding leaching rates were 14.80 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for \u003cem\u003eMaha\u003c/em\u003e and while it was only 0.55 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for \u003cem\u003eYala\u003c/em\u003e, demonstrating lower leaching risks in drier conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimulation results suggested that modest increases in total nitrogen inputs up to 345 kg N ha⁻\u0026sup1; for \u003cem\u003eMaha\u003c/em\u003e and 435 kg N ha⁻\u0026sup1; for \u003cem\u003eYala\u003c/em\u003e could slightly improve yields up to 2625.10 kg ha⁻\u0026sup1; in \u003cem\u003eMaha\u003c/em\u003e and 3174.46 kg ha⁻\u0026sup1; in \u003cem\u003eYala\u003c/em\u003e, while decreasing leaching losses in \u003cem\u003eMaha\u003c/em\u003e to 11.3 kg ha⁻\u0026sup1;, and maintaining relatively constant minimal leaching level in \u003cem\u003eYala\u003c/em\u003e. This indicates potential to enhance nitrogen use efficiency for present climate conditions through optimized application rates, particularly in the \u003cem\u003eMaha\u003c/em\u003e season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 2050 climate scenario, nitrogen application for the \u003cem\u003eMaha\u003c/em\u003e season remained the same as in the present evaluation at 345 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, yielding 2650.03 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while leaching losses increased to 13.75 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e due to projected climate variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the \u003cem\u003eYala\u003c/em\u003e season, total nitrogen remained 435 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a yield increase to 3182.09 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and leaching losses rising slightly to 0.65 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The summary analysis of the 2050 scenario suggested that slightly higher yield than the present, however with a cost of increased nitrogen leaching in both seasons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e By 2100, adjustments to nitrogen rates were necessary to account for projected climate impacts. In the \u003cem\u003eMaha\u003c/em\u003e season, the total nitrogen applied increased to 375 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, resulting in a yield of 2675.05 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with leaching losses of 14.17 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Similarly, in the \u003cem\u003eYala\u003c/em\u003e season, the total nitrogen applied increased to 465 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, producing a yield of 3198.57 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with leaching losses rising to 1.05 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. 2100 scenario evaluations highlighted the increasing challenges of maintaining yields while mitigating environmental impacts under changing climatic conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe findings of this study emphasize the critical importance of adopting dynamic fertilizer recommendations tailored to seasonal and climatic variability. Site-specific nitrogen management, rather than generalized recommendations, is essential for optimizing yields and minimizing environmental losses. Optimized nitrogen fertilizer rates have been shown to significantly increase maize productivity while reducing reactive nitrogen losses, such as N₂O emissions, NH₃ volatilization, and nitrate leaching (Du et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This aligns with findings by (Zhang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who demonstrated the significant economic benefits of nitrogen optimization in maize production.\u003c/p\u003e \u003cp\u003eHowever, nitrogen optimization alone is insufficient to ensure sustainable maize production. Other critical factors such as water management, soil health, and climate-smart practices which play a pivotal role in enhancing productivity and resilience. Integrated approaches that combine split fertilizer applications (Ojeniyi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)), irrigation management (Zhang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and soil conservation techniques (Allart et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) are essential to improving nitrogen use efficiency and reducing environmental losses. This supports the argument by (Zhou, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) that holistic strategies are necessary for sustaining crop productivity under changing climates.\u003c/p\u003e \u003cp\u003eThe use of localized data for weather, soil, and crop parameters was a key strength of this study, as it enabled reliable predictions and actionable insights. These findings align with (Pasquel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who emphasized the importance of site-specific data for refining agricultural practices. Tailoring fertilizer rates to match local conditions and future climatic scenarios minimizes environmental risks, such as nitrogen leaching, which tends to increase with higher nitrogen application rates (Yao et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Balanced fertilizer use, integrated with climate-resilient strategies, is therefore crucial to mitigating these risks while sustaining productivity.\u003c/p\u003e \u003cp\u003eThis research underscores the value of simulation modeling as a tool for developing location-specific recommendations. By integrating simulation outputs with on-farm practices, farmers and policymakers can enhance nitrogen use efficiency, reduce environmental losses, and sustain maize productivity under changing climatic conditions. Future studies should explore the integration of water management, soil conservation, and adaptive cropping systems to complement nitrogen optimization, addressing its limitations when used in isolation.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, dynamic fertilizer recommendations must be coupled with farmer education and capacity-building programs to promote the adoption of climate-smart practices (Bhanuwanti et al., 2024). Equipping farmers with knowledge and tools for integrated nutrient management, efficient water use, and soil health improvement is essential to fostering resilient agricultural systems that can cope with climate variability. Financial incentives, such as subsidies for high-quality seeds and fertilizers and access to affordable credit for smallholder farmers, can further enhance the economic feasibility of adopting these practices (Dulanjani \u0026amp; Shantha, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These measures would ensure improved productivity, environmental sustainability, and long-term viability in the agricultural sector.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAdjustments in nitrogen application timing were suggested to align with altered rainfall patterns. Reduced basal fertilizer and increased nitrogen application four weeks after sowing improved nutrient uptake and NUE during critical growth stages. Despite these optimizations, nitrogen leaching increased under future scenarios, especially during the Maha season, due to higher rainfall intensity. The study emphasized the need for climate-resilient nitrogen management strategies that integrate water management, soil health, and climate-smart practices to enhance resilience and minimize environmental impacts. Future research should focus on optimizing nitrogen under water-limited conditions and refining recommendations using localized soil and climate data. In conclusion, the research provided a framework for adaptive nitrogen management that balances productivity and environmental sustainability, enabling farmers and policymakers to address climate challenges while ensuring long-term agricultural viability.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllart K, Almoussawi A, Kerbey L, Catterou M, Roger D, Mortier D, Blanc E, Robert B, Spicher F, Emery L, Hirel B, Dubois F, Tetu T (2023) Splitting Nitrogen Fertilization Is More Important than Nitrogen Level When Mixed Wheat Varieties Are Cultivated in a Conservation Agriculture System. Agronomy 13(5):1295. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy13051295\u003c/span\u003e\u003cspan address=\"10.3390/agronomy13051295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmarasingha RPRK, Suriyagoda LDB, Marambe B, Rathnayake WMUK, Gaydon DS, Galagedara LW, Punyawardena R, Silva GLLP, Nidumolu U, Howden M (2017) Improving water productivity in moisture-limited rice-based cropping systems through incorporation of maize and mungbean: A modelling approach. Agric Water Manage 189:111\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2017.05.002\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2017.05.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhanuwanti, Dar KA, Singh L, Saad AA, Rai U, Tanwar H, Khatoon A (2024) Climate Smart Agriculture: Innovating Sustainable Practices for a Changing Climate. J Sci Res Rep 30(11):568\u0026ndash;576. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9734/jsrr/2024/v30i112585\u003c/span\u003e\u003cspan address=\"10.9734/jsrr/2024/v30i112585\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBijay-Singh, Craswell E (2021a) Fertilizers and nitrate pollution of surface and ground water: An increasingly pervasive global problem. SN Appl Sci 3(4):518. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s42452-021-04521-8\u003c/span\u003e\u003cspan address=\"10.1007/s42452-021-04521-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBijay-Singh, Craswell E (2021b) Fertilizers and nitrate pollution of surface and ground water: An increasingly pervasive global problem. SN Appl Sci 3(4):518. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s42452-021-04521-8\u003c/span\u003e\u003cspan address=\"10.1007/s42452-021-04521-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalvin K, Dasgupta D, Krinner G, Mukherji A, Thorne PW, Trisos C, Romero J, Aldunce P, Barrett K, Blanco G, Cheung WWL, Connors S, Denton F, Diongue-Niang A, Dodman D, Garschagen M, Geden O, Hayward B, Jones C, Ha M (2023) \u003cem\u003eIPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland.\u003c/em\u003e (P. Arias, M. Bustamante, I. Elgizouli, G. Flato, M. Howden, C. M\u0026eacute;ndez-Vallejo, J. J. Pereira, R. Pichs-Madruga, S. K. Rose, Y. Saheb, R. S\u0026aacute;nchez Rodr\u0026iacute;guez, D. \u0026Uuml;rge-Vorsatz, C. Xiao, N. Yassaa, J. Romero, J. Kim, E. F. Haites, Y. Jung, R. Stavins, \u0026hellip; C. P\u0026eacute;an, Eds.; First). Intergovernmental Panel on Climate Change (IPCC). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59327/IPCC/AR6-9789291691647\u003c/span\u003e\u003cspan address=\"10.59327/IPCC/AR6-9789291691647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Y, Lu Y, Guo S, Wang R, Song X, Ju X (2024) Enhanced efficiency nitrogen fertilizers (EENFs) can reduce nitrous oxide emissions and maintain high grain yields in a rain-fed spring maize cropping system. Field Crops Res 312:109408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2024.109408\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2024.109408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDulanjani PA, Shantha AA (2022) The Impact of Fertilizer Subsidy on Average Paddy Yield in Sri Lanka. Sri Lanka J Social Sci Humanit 2(2):105\u0026ndash;116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4038/sljssh.v2i2.77\u003c/span\u003e\u003cspan address=\"10.4038/sljssh.v2i2.77\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLakmali AWS, Dissanayake DMD, Malaviarachchi MAPWK, Jayarathna SRGHSM (2024) Estimating the Plant Nutrient Uptake of Selected Maize Varieties in Sri Lanka\u0026rsquo;s Dry Zone at Various Fertilizer Rates. Asian J Res Agric Forestry 10(3):157\u0026ndash;167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9734/ajraf/2024/v10i3308\u003c/span\u003e\u003cspan address=\"10.9734/ajraf/2024/v10i3308\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMapa R (2010) \u003cem\u003eSoils of the Dry Zone of Sri Lanka\u003c/em\u003e. Mapa, R.B., Somasiri S., Dassanayake, A.R. (2010). Soil of the Dry zone of Sri Lanka: Morphology, characterization and classification. 1st edition, \u003cem\u003eSoil Science Society of Sri Lanka\u003c/em\u003e, Peradeniya\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNgala AL, Kwari D, J., Makinta G (2023) Determination of Most Limiting Nutrient for Maize Yield through Nutrient Omission Experiment for Site-Specific Nutrient Recommendation on Typic ustipssament of Maiduguri, Northeast Nigeria By. Acta Sci Agric 7(8):25\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31080/ASAG.2023.07.1284\u003c/span\u003e\u003cspan address=\"10.31080/ASAG.2023.07.1284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOjeniyi K, Ngonidzashe C, Devkota K, Madukwe D (2024) Optimizing split-fertilizer applications for enhanced maize yield and nutrient use efficiency in Nigeria\u0026rsquo;s Middle-belt. Heliyon 10(19):e37747. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e37747\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e37747\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasley H, Wenham K, Bell L, Huth N, Holzworth D, Chaki A, Gaydon D, Collins M (2023) APSIM next generation mungbean model: A tool for advancing mungbean production. Field Crops Res 298:108955. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2023.108955\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2023.108955\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasquel D, Cammarano D, Roux S, Castrignan\u0026ograve; A, Tisseyre B, Rinaldi M, Troccoli A, Taylor JA (2023) Downscaling the APSIM crop model for simulation at the within-field scale. Agric Syst 212:103773. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agsy.2023.103773\u003c/span\u003e\u003cspan address=\"10.1016/j.agsy.2023.103773\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRathnayake RMDH, Dharmawardhana DTPS (2025) Adoption of Best Cultivation Practices and Post-Harvest Techniques among Maize Farmers in Anuradhapura District, Sri Lanka. J Multidisciplinary Translational Res 10(1):66\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4038/jmtr.v10i1.81\u003c/span\u003e\u003cspan address=\"10.4038/jmtr.v10i1.81\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusinamhodzi L, Berre D, Thierfelder C, Ridaura S-L, Corbeels M (2025) Modelling the climate change adaptation potential of no-tillage maize systems in southern Africa. Mitig Adapt Strat Glob Change 30(5):36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11027-025-10223-y\u003c/span\u003e\u003cspan address=\"10.1007/s11027-025-10223-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamarasinghe BKDJR, Zhu Y, Abeynayake NR, Zeng X, Mathavan B, Wanninayake RWWMPK, Rasheed S, Bah AS (2025) Climate change and rice production in Sri Lanka: Short-run vs. long-run symmetric and asymmetric effects. Front Sustainable Food Syst 9:1592542. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fsufs.2025.1592542\u003c/span\u003e\u003cspan address=\"10.3389/fsufs.2025.1592542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao Z, Zhang W, Chen Y, Zhang W, Liu D, Gao X, Chen X (2021) Nitrogen leaching and grey water footprint affected by nitrogen fertilization rate in maize production: A case study of Southwest China. \u003cem\u003eJournal of the Science of Food and Agriculture\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jsfa.11263\u003c/span\u003e\u003cspan address=\"10.1002/jsfa.11263\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Zhang W-S, Meng Q-F, Hu Y-C, Schmidhalter U, Zhong C-H, Zou G-Y, Chen X-P (2023) Optimizing Agronomic, Environmental, Health and Economic Performances in Summer Maize Production through Fertilizer Nitrogen Management Strategies. Plants 12(7):1490. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants12071490\u003c/span\u003e\u003cspan address=\"10.3390/plants12071490\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Zhang Y, Shi Y, Yu Z (2020) Optimized split nitrogen fertilizer increase photosynthesis, grain yield, nitrogen use efficiency and water use efficiency under water-saving irrigation. Sci Rep 10(1):20310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-75388-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-75388-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou M (2016) Sustaining crop productivity while reducing environmental nitrogen losses in the subtropical wheat-maize cropping systems: A comprehensive case study of nitrogen cycling and balance. \u003cem\u003eAgriculture Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e231\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agee.2016.06.022\u003c/span\u003e\u003cspan address=\"10.1016/j.agee.2016.06.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Rajarata University of Sri Lanka","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"APSIM maize model, climate change, climate-specific optimization, leaching, nitrogen management","lastPublishedDoi":"10.21203/rs.3.rs-8737594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8737594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) is the most vital upland cereal and a cornerstone of food security in Sri Lanka. The crop is extensively cultivated in the dry zone, a region highly vulnerable to climate change. Nevertheless, current nitrogen (N) fertilizer rates have not been recently climate-optimized, reducing yield efficiency and increasing environmental impacts. The objectives of this study were to model and optimize N fertilizer rates for maize, under current and future climate conditions for 2050 and 2100 by testing different nitrogen management scenarios by utilizing the Agricultural Production Systems Simulator (APSIM). Simulations were conducted using 30 years of past climate data from \u003cem\u003eMahailluppallama\u003c/em\u003e local weather station and 2023 IPCC climate projections to test multiple nitrogen management strategies. Results showed that the current Department of Agriculture recommendation of 315-425 kg ha⁻¹ N can be optimized to 345-435 kg ha⁻¹ under present conditions, increasing yields while reducing leaching losses. Under future climates, optimal N rates rose slightly to 375-465 kg ha⁻¹ to maintain productivity, though leaching risks increased. Findings highlight the importance of adaptive, climate-responsive N management strategies to sustain maize yield and minimize environmental impacts. The outcomes provide valuable guidance for developing data-driven, climate-smart fertilizer recommendations for dry zone of the Sri Lanka.\u003c/p\u003e","manuscriptTitle":"Model-Assisted Optimization of Nitrogen Fertilizer Rates for Maize in Dry Zone of Sri Lanka under Current and Future Climate Scenarios","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 06:11:00","doi":"10.21203/rs.3.rs-8737594/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ffab61e-039f-44f4-bddd-a7cdfdc30ca9","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62008703,"name":"Climate Analysis and Modeling"},{"id":62008704,"name":"Agronomy"},{"id":62008705,"name":"Agricultural Engineering"},{"id":62008706,"name":"Horticulture"}],"tags":[],"updatedAt":"2026-02-02T06:11:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 06:11:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8737594","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8737594","identity":"rs-8737594","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}