Nitrogen management for hybrid canola in Canada

Hybrid canola cultivars were first introduced in 1989 and are widely grown in Canada due to their high yield potential and improved N use efficiency. For example, in trials at Scott, Melfort, and Indian Head, Saskatchewan, canola hybrid InVigor 2663 produced 16.5% higher yields than the open-pollinated cultivar Quantum at the same 134 lb N/ac application rate (Mahli et al., 2007). This high-yielding characteristic requires substantial N input to support vigorous crop growth and enhance oil and protein accumulation (Ma et al., 2015; Ma & Herath, 2016), which can mask improvements in N use efficiency achieved from advanced hybrid breeding, thereby increasing environmental risks and carbon footprint (Ma et al., 2023). Because hybrids are constantly being updated every three to five years and N management guidance for modern hybrid canola is limited, a thorough understanding of its yield response to N supply is critical. This knowledge empowers producers to optimize fertilization while improving crop productivity and environmental stewardship goals.

As a cool-season crop, canola is highly sensitive to heat and drought stress. Reduced precipitation limits root growth, and heat stress coupled with drought reduces stomatal conductance and photosynthesis, minimizing the formation of yield components such as silique number and seed weight, ultimately affecting crop productivity and quality (Biswas et al., 2019; Wu et al., 2018). Moreover, N fertilizers are typically applied before planting or before the crop begins to actively grow—a stage at which these physiological and metabolic responses have yet occurred (Ma et al., 2015, 2020; Ma & Zheng, 2016). These uncertainties make providing accurate and effective N recommendations particularly challenging. 

To address these scientific and practical challenges, this study aims to investigate the response of new hybrid canola cultivars to N fertilization in diverse Canadian agroecosystems; and to introduce an AI-powered tool designed to deliver site-specific N recommendations early in the growing season.

To achieve these objectives, field experiments were carried out from 2013 to 2022 at 50 sites-years across Canada (Figure 1). At each site-year, a factorial experiment with two hybrids and eight N treatment combinations was arranged in a randomized complete block design with four replications. Canola hybrids and N treatments used in this study are listed in Table 1. 
 

^{Note: Hybrid cultivars varied by location and growing season. For both the single N treatment and the initial application of 45 lb N/ac in the split-N treatment, fertilizer was broadcast and incorporated into the soil at planting. For split-N application, the remaining N was topdressed manually at the four- to six-leaf stage.} 

Soil properties, including organic matter, pH and cation exchange capacity at preplant, and soil mineral N both before planting and after harvest were measured to determine their impact on crop production. Plant health and N status were monitored using a SPAD-502 leaf chlorophyll meter and canopy reflectance (expressed as Normalized Difference Vegetation Index; NDVI) with GreenSeeker (Wu et al., 2022). Canola yields were recorded at harvest, and N use efficiency (lb seed /lb N input) was calculated:
 

Yield responses to nitrogen fertilizer application generally follow a quadratic pattern. Therefore, EONR is calculated following the procedure shown in Figure 2. 
 

Using field soil data, plant health, and weather data, we developed machine-learning models, including support vector machine and random forest. Implemented with scikit-learn, the models were optimized via grid search with tenfold cross-validation and evaluated using leave-one-out cross-validation, focusing on Pearson correlation as a key performance metric (Wen et al., 2021; 2022).

Nitrogen management

Yield–nitrogen response

The yield response of hybrid canola to N supply is strongly influenced by growing season weather conditions. With adequate and evenly distributed rainfall, canola yield increased significantly by an average of 35% at N application rates from 0 to the EONR with estimated EONR canola yields meeting or exceeding the 52 bu/ac benchmark set by the Canola Council of Canada. In contrast, in regions with limited and erratic rainfall distribution, such as Swift Current, SK, canola yields were consistently lower, regardless of N application (Figure 3).
 

In this study, more than 40% of the studied sites-years experienced some degree of moderate to severe drought stress (total growing season rainfall <85% of the 30-year average). Interestingly, fields with high soil organic matter (SOM), such as those in Melfort (SK), Beaverlodge (AB), and Odds (AB), were able to buffer against mild to moderate drought stress and still increase yields with increased N input. This resilience is likely due to the soil’s superior water-holding capacity (Ma et al., 2015, 2020; Wen et al., 2023). However, under severe and extreme drought conditions, neither high SOM nor increased N application can prevent yield losses—canola yields may drop by up to 80% compared with those under optimal conditions (Wen et al., 2022, 2023). This occurs because dry soil restricts root growth and limits nutrient uptake (Wu et al., 2018), which in turn slows or even stops key physiological processes such as photosynthesis (Biswas et al., 2019), reducing plant pod and seed development (Figure 4; Wen & Ma, 2024).
 

Under high-yielding conditions, hybrid canola requires more N than open-pollinated cultivars to support greater biomass production and higher seed oil and protein content. An earlier two-year field study in Melfort, SK (also a trial site for our study) showed that hybrid cultivars produced significantly higher yields than the open-pollinated cultivar at similar N rates (Mahli et al., 2007). In our study, despite higher N requirements, all hybrids showed impressive N use efficiency when N was applied at economic optimum nitrogen rate (EONR) levels, producing 28 lb of seed per pound of N applied over 22 normal site-year environments (Figure 3b). Compared with earlier findings at the same site by Mahli et al. (2007), whose open-pollinated cultivar produced approximately 18 lb of seed for each lb of N applied. However, we also note that under low precipitation conditions, small amounts of single N application at planting combined with soil residual N and in-season mineralization may be sufficient to meet crop needs. In this case, the N use efficiency of the tested hybrids dropped sharply, producing only 2–16 lb of seed per lb of N fertilizer applied, as evidenced by 20 site-year trials. This highlights the risk of over-application of N in dry conditions, which can lead to increased losses and greater environmental impacts.

Split-N application

The mismatch between soil N supply and crop growth curves is a key cause of low N use efficiency, as crops are unable to effectively absorb soil available N relative to their needs (Bijay-Singh et al., 2024). For example, a single preplant application of N can create a large mineral N pool in the soil, resulting in a high risk of N loss through N₂O emissions, NH₃ volatilization and NO₃- leaching (Ma et al., 2010). This is because in the early stages of vegetative growth, canola crops grow slowly, and it takes 4-6 weeks after emergence for the crop roots to quickly absorb N (Ma and Zheng, 2016). 

Split N application—dividing total N into a small initial dose and a second dose during the rapid growth phase of the crop—offers a promising strategy for reducing the risk of overfertilization and crop lodging (Wu & Ma, 2022; Wu et al., 2022) and matching crop growth and N uptake curves (Ma & Zheng, 2016), especially during seasons when drought or unpredictable weather is expected. The main advantage of this strategy is the flexibility to adjust topdressing according to in-season conditions. For example, under normal weather conditions (Figure 5a), such as at Beaverlodge in 2018, a split-N strategy increased canola yield by an average of 5% in 38 cases across 50 site-year environments compared with the single-N strategy at the same N rate. In comparison, Swift Current's two-year average EONR was only 31 lb N/ac (Figure 3a), meaning that an application of 45 lb N/ac at planting was sufficient to meet crop growth requirements without the need for additional topdressing. 
 

This strategy can also benefit producers by saving on N input costs and provide the public with clean air by preventing N loss into the environment, thus contributing to a sustainable environment. However, exceptions did occur during dry or unevenly distributed rainfall seasons, such as at Melfort in 2019 where the split-N strategy resulted in slightly lower yields and lower N use efficiency in 8 out of the 50 site-year environments (Figure 5b). This may be due to excessive early-season rainfall leading to N leaching from the root zone as well as the initial application rate of 45 lb N/ac being insufficient to support vigorous early crop growth. Nitrogen deficiency at the vegetative stage can limit root development, weakening the plant's resilience to mid-season drought (Ma and Zheng, 2016). Drought after topdressing limits the availability of in-season applied N and may result in reduced crop N utilization. 

Currently, adoption of split-N application strategy among Canadian canola producers is low with only about 5% of farms using this strategy according to the Canadian 2023 Fertilizer Use Survey (Fertilizer Canada, 2023). This strategy is highly adaptable, allowing initial dosage to be fine-tuned to meet early growth needs while retaining the option of topdressing based on real-time weather patterns. During early-season drought conditions, initial N applications can be reduced, and if conditions improve later, supplemental N fertilizer can be applied to support continued growth, thus striking a balance between economic input, crop productivity, and environmental responsibility. 

Nitrogen management tool

These findings highlight the need to tailor N application to site-specific conditions to achieve sustainable canola production. With its powerful predictive capacities, artificial intelligence (AI) is increasingly used in agriculture and shows great potential to provide precise early-season N recommendations (Wen & Ma, 2024). In this study, we developed AI models using historical weather (e.g., past 10 years of precipitation), soil, field management (fertilizer rate, preceding crop, etc.), and crop growth data (leaf chlorophyll content and NDVI) to predict site-specific EONR for hybrid canola (Wen et al., 2021, 2022). Among the models tested, random forest performed best with an R² of 0.90 and a relative error of 2% compared with the EONR value calculated from the yield–N response curve (Figure 6). This shows that the AI-based model effectively classified seasonal rainfall and adjusted N recommendations accordingly, especially under moderate to favorable rainfall conditions. 
 

The tool, still under development, is expected to incorporate soil mineral N, soil moisture, cation exchange capacity, and soil conductivity, etc. into dynamic models based on deep learning. The integration of AI models with split-N application strategies is expected to support early informed decision-making to increase yield and reduce environmental impact, thereby advancing precise, sustainable N management for hybrid canola. 

In conclusion, this multi-site-year study shows that the EONR for hybrid canola varies significantly depending on prevailing weather, soils, and agroecosystems with different rainfall patterns. Based on random forest model predictions, an EONR of 130–150 lb N/ac is recommended for Canadian prairie black soil zones, reflecting higher yield potential and higher SOM. In contrast, low-yielding brown soil zones, which are more prone to drought and have lower SOM, require a lower EONR of 75–90 lb N/ac. Transitional soil zones, such as the brown–black junction areas, and regions like Ontario typically require about 107–134 lb N/ac. The AI-enhanced dynamic model enables canola producers to make real-time adjustments based on early-season weather, soil, and crop-growing conditions, employing split-N fertilizer strategies and fine-tuning actual application rates to optimize crop yields, enhance production efficiency and profitability while minimizing environmental impact.