Impact of Precision Nutrient Management on Rice Growth and Productivity in Southern Odisha

Rice serves as a primary food crop for more than half of the world’s population and the introduction of high-yielding and fertilizer-responsive varieties during the Green Revolution played a crucial role in ensuring global food security (Fahad et al., 2019). With the projected population growth of 9 billion by 2050, there is a pressing need to increase rice production while dealing with limited land resources (Timsina et al., 2023). Effective fertilizer management is a vital factor in improving soil fertility and narrowing the yield gap.
       
During the early years of the Green Revolution, there was a notable rise in agricultural yields as a result of the increased use of fertilizers (EliazerNelson et al., 2019). However, in subsequent years, the growth in yields experienced to slow down comparatively. Proper nutrient supply is essential for optimal soil and crop management and increasing fertilizer inputs significantly enhance rice productivity (Rollon et al., 2021). However, farmers often apply excessive nitrogen and insufficient amounts of phosphate and potassium fertilizers, leading to increased pest prevalence, diseases and lodging (Aziz et al., 2018).
       
Standardized fertilizer recommendations do not adequately provide the optimal quantity of nutrients required for agricultural systems, leading to either excessive or insufficient nutrient application. This limitation hampers the efficient utilization of nutrients in crop production (Sanyal et al., 2014). Excessive fertilizer application can also lead to nutrient leakage and environmental contamination, including surface and groundwater pollution (Craswell, 2021). To address these challenges and maintain long-term environmental and agricultural sustainability, the adoption of precision nutrient management in rice cultivation has become crucial (Parihar et al., 2020 and Sharma et al., 2019). This approach involves synchronizing fertilizer application with crop needs. Numerous smart tools and decision support systems are available to determine the precise timing and appropriate dosage of primary nutrients tailored to specific sites. These tools rely on continuous monitoring and surveying to optimize nutrient management strategies. To date, there has been limited research on assessing the most effective smart or decision support tool for synchronizing fertilizer application with crop needs while ensuring optimal food production. Therefore, the current study aims to address this research gap.

The research was conducted during the boro season of 2021-22 at the PG Experimental Farm of M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management in Odisha, India. The experimental farm is situated at coordinates 18°48'18"N and 84°10'45"E, with an elevation of 88 meters above sea level. The study was carried out under irrigated conditions.
       
The soil in the experimental field was sandy loam in texture and slightly acidic, with a pH value of 6.23. The soil had low levels of organic carbon (0.34%) and available nitrogen (176 kg/ha), while phosphorus (16 kg/ha) and potassium (127 kg/ha) levels were considered medium. The rice variety Naveen (IET 14461, CR 749-20-2) was sown on December 8, 2021, using a seed rate of 40 kg/ha in the nursery. The seedlings were later transplanted to the main field on January 7, 2022. The application of fertilizers was carried out according to the prescribed treatments (Table 1).
 

       
The experiment followed a randomized block design, with eleven treatments that were replicated three times. The investigated treatments included the following: T₁-absolute control (no fertilizer), T₂-75% of the recommended dose of fertilizer (RDF), T₃-100% RDF, T₄-125% RDF, T₅-75% RDF followed by a spray of nano urea at a rate of 2 ml/L during panicle initiation, T₆-100% RDF followed by a spray of nano urea at a rate of 2 ml/L during panicle initiation, T₇-leaf colour chart (LCC) 3 based nitrogen management,, T₈-LCC 4 based nitrogen management, T₉-Sufficiency Index (SI)-based nitrogen management at SI<90%, T₁₀- Nutrient expert (NE) based nutrient recommendation and T₁₁- Rice crop manager (RCM) based nutrient recommendation.
       
The collected experimental data for different parameters were subjected to statistical analysis using analysis of variance (ANOVA). The standard error of means (SEm±) and the critical difference at a significance level of p = 0.05 were calculated for the analysis. The methodology employed for statistical analysis was based on the approach outlined by Gomez and Gomez (1984).

Growth parameters
 
Precision nutrient management had a significant impact on the plant height and dry matter accumulation per square meter in rice at harvest, as indicated in Table 2. The SI-based N management treatment resulted in the highest plant height and dry matter accumulation. In terms of plant height and dry matter accumulation, these treatments exhibited a substantial increase of 26.01% and 80.58%, respectively, compared to the absolute control, which had the lowest values among all the treatments being compared. Regarding plant height, treatments such as the 125% RDF, LCC 4 based nitrogen management, 100% RDF followed by nano urea spray at 2 ml/L during panicle initiation, 100% RDF, RCM-based nutrient recommendation and NE-based nutrient recommendation showed comparable results to the superior treatment. Similarly, for dry matter accumulation, the 125% RDF treatment showed comparable performance to the superior treatment. The provision of nutrients in a timely manner and in sufficient amounts supports the synthesis of protoplasm and promotes vigorous growth of the plant. When nitrogen levels are increased, especially with adequate supplies of phosphorus and potassium, the plant is able to absorb nutrients more effectively, leading to rapid expansion of foliage and ultimately promoting overall growth. The results obtained were similar to the earlier findings (Subedi et al., 2019; Ali et al., 2015 and Bhat et al., 2022).


 
Yield and yield attributes
 
Precision nutrient management had a significant impact on all yield components of rice, except for test weight. Results from Table 3 showed that the number of effective tillers per square meter, number of spikelets per panicle and number of filled spikelets per panicle were notably affected by different precision nutrient management treatments. In particular, the SI-based N management treatment demonstrated superior performance, exhibiting a 34.09% increase in the number of effective tillers per square meter, a 40.22% increase in the number of spikelets per panicle and a remarkable 52.70% increase in the number of filled spikelets per panicle compared to the absolute control. It is worth noting that the test weight, being a stable varietal characteristic, did not exhibit significant variations due to precision nutrient management. The appropriate timing of nitrogen application through consistent monitoring could have contributed to a prolonged period of leaf greenness. This, in turn, resulted in increased levels of active photosynthesis during the ripening stage. The extended photosynthetic activity was the primary factor behind the larger capacity of the grain storage area and a reduced number of unfilled spikelets. These findings are in conformity with Moharana et al., (2017); Pradhan et al., (2021) and Hemalata et al., (2020).
 

       
The application of precision nutrient management had a significant impact on various yield parameters of rice as indicated in Table 4. The results clearly demonstrated that nitrogen management based on the sufficiency index (SI) led to significantly higher grain and biological yield than absolute control and it was closely followed by the application of 125% recommended dose of fertilizer (RDF). However, when it came to straw yield, 125% RDF outperformed the other treatments, but was comparable to SI-based nitrogen management. The implementation of SI-based nitrogen management resulted in a 16.68% increase in grain yield and a 13.17% increase in biological yield of rice, compared to the application of 100% RDF at fixed time intervals. The possible reason for the increased grain yield could be attributed to the improved growth and yield attributing characters, leading to larger grain storage capacity and effective transportation of nutrients from the source to the grain. This could have resulted in a higher number of grains being filled, thereby increasing the overall grain yield. Similar findings were reported by Santos et al., (2019; Singh and Khind (2015) and Hussain et al., (2000). On the other hand, the application of 125% RDF led to a 10.68% increase in straw yield compared to 100% RDF and a substantial 65.11% increase compared to the absolute control. However, the straw yield obtained from 125% RDF was comparable to that obtained from 100% RDF and harvest index was not influenced significantly by different precision nutrient management practices under comparison.
 

The findings of this study strongly indicate that implementing SI-based nitrogen (N) management, along with recommended amounts of phosphorus and potassium, can be a successful approach for achieving increased grain yield in rice cultivation in Southern Odisha. SI-based N management closely matched the performance of applying 125% recommended dose of fertilizer (RDF), but with SI-based management, slightly lower quantities of phosphorus and potassium (approximately 15 kg less) were required. This suggests that SI-based N management at (SI<90%) can be an efficient alternative with comparable yield outcomes while optimizing the use of nitrogen. phosphorus and potassium fertilizers.

Authors do not have any conflict of interest.