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Evaluation of Nutrient Levels, Optical Sensors and Decision Support Tools for Nitrogen Optimization in Rice during Dry Season 

Lalichetti Sagar1,*, Sagar Maitra1, Sultan Singh2, Masina Sairam1, Angara Pavan1
1Department of Agronomy, Centurion University of Technology and Management, Odisha-761211, India.
2Department of Agronomy, Sri Karan Narendra Agricultural University, Jobner-303 328, Rajasthan, India.

Background: Improving the efficiency of nitrogen utilization in rice is vital for achieving significant crop yields with minimum harm to the environment. To tackle this challenge, various portable optical sensors and decision-support tools have been emerging in recent times. However, there is currently a lack of comprehensive assessment and comparison of these tools with different levels of fertilizer recommendation with and without additional supplementation of nano urea.

Methods: The current study was carried out during the dry season of 2021 and 2022 at the P.G. Experimental Farm of Centurion University of Technology and Management. The investigated treatments encompassed the following: T1: Absolute control, T2: 90-45-45 (N-P2O5-K2O kg/ha), T3: 120-60-60 (RDF),  T4: 150-75-75, T5: T2+NU @ 2 ml/L at panicle initiation, T6: T3+NU @ 2 ml/L at panicle initiation, T7: N application at LCC d™ 3, T8: N application at LCC d™4, T9: N application at SI < 90%, T10: Nutrient Expert (TY: 5.5 t/ha), T11: Rice crop manager (TY: 5.5t/ha) which were allocated in randomized block design with three replications.

Result: The findings showed that implementing nitrogen (N) application when the sufficiency index (SI) is below 90% led to a significant 16.85% boost to grain yield of rice compared to the application of 120-60-60 at fixed intervals as per the standard recommendation. In the former approach, 150 kg N/ha was applied in four splits. However, N application at a leaf color chart (LCC) value of 4 or less was considered efficient, with maximum agronomic efficiency (18.22 and 19.22 kg grain/kg N applied) and recovery efficiency (43.27% and 50.25%) observed in 2021 and 2022, respectively since, this approach resulted in a comparable yield increase and nitrogen absorption with a reduced nitrogen input.

A majority of the global population relies on rice as their primary dietary staple. In India, rice is cultivated on approximately 45.77 million hectares of land, yielding 124.37 million tonnes of rice with an average productivity of 2717 kg/ha (Anonymous, 2023a). In the state of Odisha, rice is grown on 4.04 million hectares, producing 8.81 million tonnes of rice at an average productivity of 2182 kg/ha (Anonymous, 2023b). Meeting the food requirements of a growing population with limited available farmland is a significant challenge to agriculture.
Efficient fertilizer management is a crucial strategy to bridge the gap between actual and potential rice crop yields (Chivenge et al., 2021). The Green Revolution demonstrated that increased nitrogenous fertilizer had a positive correlation with higher rice grain production. Nitrogen plays a crucial role in boosting rice crop growth and productivity (Keerthi et al., 2023). However, the application of nitrogen beyond the crop needs at inappropriate times results in soil degradation, water pollution and increased greenhouse gas emissions etc. (Singh et al., 2023). Hence, it is imperative to optimize the nitrogen supply and achieve maximum nitrogen use efficiency to sustain rice production.
Optimizing nitrogen in agriculture requires alignment of nitrogen fertilizer applications according to the specific needs of the crops through continuous monitoring (Swamy et al., 2022). This should be carried out in conjunction with the assurance of an adequate and well-balanced supply of phosphorus and potassium, as these elements are equally essential for enhancing the efficient utilization of nitrogen (Li et al., 2014). Taking this into consideration, different fertilizer grades, portable optical sensors and decision support tools based on nutrient management was compared to find out the most judicious combination of nitrogen, phosphorus and potassium that optimizes the nitrogen requirement in rice. Additionally, the fertilizer grades were supplemented by nano urea, a recent introduction by the Indian Farmers Fertilizer Cooperative Limited (IFFCO), to assess its unexplored.
During the dry season of 2021 and 2022, a study was conducted at P.G. Research Farm, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management. Odisha (18°48'N latitude, 84°10"E longitude with 88m height above mean sea level). The meteorological data was collected from an automatic weather station situated at Centurion University of Technology and Management, Parlakhemundi campus. The findings revealed that in 2021 (8th December, 2021 to 18th April, 2022), there was extremely limited rainfall, amounting to only 97.4 mm, while in 2022 (1st December, 2022 to 11th April, 2023), the region experienced a slightly higher total rainfall of 110 mm during the dry season. Throughout the period of investigation in both years, the maximum temperatures ranged from 27.4 to 40.7°C and the minimum temperatures varied between 12.3 and 26.7°C.
The research field had soil with a sandy loam texture and a slightly acidic pH level. The soil had low levels of organic carbon and available nitrogen but contained moderate amounts of phosphorus and potassium. The rice variety Naveen (IET 14461, CR 749-20-2), a medium duration variety with medium bold grains, evolved at the National Rice Research Institute (NRRI), Cuttack was selected for this experiment. There were a total of eleven treatments such as three graded levels of fertilizer without nano urea, two graded levels with nano urea, three nitrogen management approaches using optical sensors, two nutrient management approaches based on decision support tools and an absolute control which were allocated in randomized block design with three replications. Nitrogen, phosphorus and potassium were provided to the soil via different sources: urea for nitrogen, single super phosphate for phosphorus and muriate of potash for potassium. Additionally, IFFCO nano urea was utilized to supply nano urea. The specific fertilizer application schedule for both years is outlined in Table 1.

Table 1: Details of fertilizer application during the period of investigation.

To evaluate the treatments and optimize the nitrogen dosage of rice cultivated during the dry season at the northeastern ghat zone of Odisha the mean yield viz., grain yield (kg/ha), straw yield (kg/ha), biological yield (kg/ha), harvest index (%) and nutrient uptake in grain and straw of rice were analyzed statistically using standard error of means (SEm±) and determined the least significant difference at a significance level of p = 0.05  according to the standard methodology (Gomez and Gomez, 1984). Further, the data analysis pack of Microsoft Excel software was used for statistical analysis.
Influence of nutrient levels, optical sensors and decision support tools on yield and harvest index of rice
The study results (Table 2) indicated that the treatment T9 (N application at SI < 90%) had noted significantly higher mean grain yield of rice (5379 kg/ha) and remained statistically at par with T4 (150-75-75), T8 (N application at LCC ≤4) and T6 (T3+NU @ 2 ml/L at panicle initiation). Moreover, the former treatment reported a 16.85% significant increase in the mean grain yield of rice in comparison with T3 (120-60-60) which represented the recommended dose of fertilizer. The increased nitrogen application in treatments T9, T4 and T8, as opposed to T3, likely contributed to an expanded sink capacity through adequate availability of nitrogen at sensitive stages and subsequently led to higher grain yields. Additionally, exceeding the recommended levels of phosphorus and potassium did not have a statistically significant impact on grain yield when the nitrogen level was held constant. However, when nano urea was applied during the panicle initiation stage in conjunction with the recommended fertilizer dose, it yielded results that were statistically comparable to higher nitrogen levels. The results were similar to the findings of Cheng et al., (2022) in rice; Samanta et al., (2022) in finger millet; Samui et al., (2022) and Sairam et al., (2023) in maize. Interestingly, the mean straw yield of rice was significantly higher with T4 but registered statistically comparable results with T9, T8, T6 and exhibited 10.79% significant superiority over T3. The better performance of T4 could be explained by the relatively higher levels of nitrogen, phosphorus and potassium availability in comparison to the other treatments. The increased nutrient availability might have contributed to enhance the quality and rigidity of the rice straw. Nevertheless, the improvement in straw quality and rigidity due to these factors was statistically insignificant when compared to treatments with sufficient nitrogen supply. These results corroborate the findings of Moharana et al., (2019) and Mohanta et al., (2021). The highest mean biological yield was recorded by T9 and this result did not significantly differ from T4 and T8. In comparison with Tthe former treatment showed significant superiority and recorded a 13.13% increment in the biological yield of rice. This might be attributed to the maximum grain and straw yield of rice. Among all the treatments the lowest mean grain yield, mean straw yield and mean biological yield of rice was recorded by the T1 (control) treatment. Although there are numerical differences among the treatments concerning harvest index (%), however, they did not differ statistically. Similar findings were observed by Awan et al., (2011) in rice; and Panda et al., (2021) in finger millet.

Table 2: Influence of different nutrient levels, portable optical sensors and decision support tools on yield and harvest index of rice (Mean of 2021 and 2022).

Influence of nutrient levels, optical sensors and decision support tools on nitrogen uptake in grain and straw of rice
The data about nitrogen uptake in grain and straw of rice were significantly influenced by different fertilizer levels, portable optical sensors and decision support tools (Table 3). The uptake of any nutrient is mainly determined by yield and nutrient content in its dry matter. In this study, the treatment T9 outperformed all other treatments by showing significantly higher nitrogen uptake in grain during the crop-growing season in 2021 and 2022. Notably, the performance of T9 in terms of nitrogen uptake by rice grain was statistically equivalent to those of T4, T8 and T6 during both the respective years of investigation 2021 and 2022. This superior performance might be attributed to the former treatment, through its grain yield and enhanced nitrogen concentration in the grain dry matter due to precise and consistent nitrogen supply at adequate levels of phosphorus and potassium through real-time monitoring of chlorophyll index at regular intervals. These findings corroborate with the findings of (Moharana et al., 2019; Baral et al., 2021)

Table 3: Influence of different nutrient levels, portable optical sensors and decision support tools on nitrogen uptake in grain and straw of rice.

However, it’s worth mentioning that T4 recorded the highest nitrogen uptake in rice straw in 2021 and 2022, respectively. Apart from T9, T8, T6 and T3, all other treatments under comparison exhibited a significant difference in nitrogen uptake by straw in 2021 with the former treatment while, T9, T8, T6, T11 and T3 showed statistical similarity but the remaining treatments exhibited significant difference in nitrogen uptake by straw in 2022. The absorption of nitrogen by rice straw was primarily determined by the quantity of straw produced and the nitrogen content left within the straw after contributing to grain production. These factors, in turn, were predominantly affected by the provision of an ideal amount of nitrogen without any restrictions on the availability of phosphorus and potassium. This discussion was in agreement with the findings of (Singh et al., 2015; Nandan et al., 2020 and Sreedevi et al., 2022).
Influence of nutrient levels, optical sensors and decision support tools on agronomic N efficiency and N recovery efficiency of rice
Among the treatments, the treatment T8 was found to register the highest Agronomic efficiency and Recovery efficiency during both the years of investigation 2021 and 2022 (Table 4). Increase in grain yield in proportion to the nitrogen applied; likely be the probable reason for recording comparatively higher agronomic efficiency by the former treatment such that for each kilogram application of nitrogen the grain yield was found to be increased by 18.22 kg in 2021 and 19.29 kg in 2022 over no fertilizer control. Likewise, the greater increase in total nitrogen uptake in proportion to the nitrogen applied may be the contributing factor for the elevated recovery efficiency and in the former treatment, it was found that only 43.27% and 50.25% shall be recovered from the total N fertilizer applied during 2021 and 2022, respectively. Similarly, Subedi et al., (2018), Baral et al., (2021) in rice reported significantly higher agronomic nitrogen use efficiency and recovery efficiency. 

Table 4: Influence of different nutrient levels, portable optical sensors and decision support tools on Agronomic N efficiency and N recovery efficiency of rice.

The research recommends using nitrogen application when the leaf colour chart (LCC) is equal to or less than 4, in combination with the recommended levels of phosphorus and potassium. This approach is statistically comparable to the treatment that excels in grain yield, as it requires comparatively less nitrogen input, resulting in higher nitrogen use efficiency compared to other treatments. Consequently, deemed to contribute towards the pursuit of sustainability.
Authors do not have any conflict of interest.

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