Determining Limitation of Nutrients for Rice Using the Compositional Nutrient Diagnosis (CND) Method

Background and Objectives
Nutrient limitations often reduce crop yield in farmers' fields. Identifying the status of rice plant nutrients in the soil and plant is necessary for plant nutrition management. In rice plant, it is common to use soil chemical properties test to understand the availability of soil nutrients. Although soil tests based on critical level may indicate the adequacy of nutrients, sometimes the rice plant in the field exhibit signs of its deficiency. Flooding conditions in rice cultivation leads to chemical and electrochemical changes in some nutrients, which result in inadequate nutrient uptake by the plant. Solely relying on soil testing in such conditions increase errors in results interpretation and fertilizer recommendations. Therefore, measuring the nutrients characteristics of both plant and soil can be effective in fertilizer recommendations. One method for interpreting leaf analysis results is the use of compositional nutrient diagnosis (CND) method. The aim of this study was to establish reference norms, the range of optimal concentration and nutrients limitation in rice plant using the CND method during 2018 growing season in 60 paddy fields of Guilan province, Iran.
 
Methodology
Soil and leaf composite samples were collected from farmers' fields in a standard manner and analyzed using appropriate laboratory methods. At harvest time, the yield of each field was determined. Nutrient indices were assessed by the compositional nutrient diagnosis (CND) method. By employing the cumulative function model of the variance ratio of nutrients and solving third-degree cumulative function equations related to 10 nutrients along with their residual concentrations, yields corresponding to each of them were calculated in terms of tons per hectare. To categorize the yield community into favorable and unfavorable groups, initially, the yield-nutrient function was plotted and the yield groups were separated by identifying the inflection points of the curve. To validate the compositional nutrient diagnosis method, the relationship between the nutrition balance index (r²) and crop yield was evaluated through a scatter plot. Descriptive statistics were conducted using SPSS version 24 and CND calculations were performed using Excel software.
 
Results
Based on the average of available phosphorus concentration and its critical level 47% of the fields exhibited phosphorus deficiency. According to the critical level of available potassium, except for one farm, the available potassium content exceeded the critical level in all fields. Due to the fact that the minimum amount of micronutrients (zinc, iron, copper, and manganese) was more than their critical level in paddy fields, indicating no deficiency in the studied soils. Available magnesium concentration ranged from 0.8 to 4.6 with the median of 2.4 cmol₊/kg. Nearly 75% of the studied soils were deficient in magnesium, based on the critical level of available magnesium (3 cmol_+/kg).
The average phosphorus concentration in leaves was 0.23% and based on the critical level, there was phosphorus deficiency in two fields. The average potassium concentration in the sample of leaves collected from the fields was 1.57% and based on the critical level, potassium deficiency was observed in rice plant tissue in 55% of the fields. The average magnesium concentration in rice leaf samples was 0.17% and six farms showed deficiency based on the magnesium critical level. The average zinc concentration was 13.69 in rice leaf samples and based on zinc critical level, 58 farms (96%) showed zinc deficiency. Based on the calculated average yields, the target yield was determined as 4133 kg/hectare, and according to the target yield, 43.3% of selected fields were classified as high-yielding and 56.6% were classified as low-yielding group. The CND method correctly recognized the balance of nutrients in rice plant with 55% accuracy. The relationship between yield and nutrients balance index showed that 31% of fields had nutritional balance, 24% had nutritional unbalanced, 22.4% had yield reduction other than nutritional factors such as climate, root depth limitations, pests, and diseases.
 
Conclusion
The findings showed that: 1- Using only the critical level of soil and plant nutrients for assessing the nutritional status of soil and plants was not efficient in the studied paddy fields. 2- The CND method, due to its consideration of the interactive effects of nutrients, had greater capability than the critical level method in diagnosing nutritional disorders, nutrient deficiencies, and fertilizer recommendations for the studied paddy fields. However, it was not the most accurate, sensitive and unique because factors other than soil fertility and rice plant nutrition, such as climatic conditions and irrigation, and field management, also influence the growth and development of rice plant and its yield. 3- The most important macronutrients and micronutrients needed in studied paddy soils based on the CND method were magnesium and phosphorus (macronutrients) and copper and zinc (micronutrients). 4- The CND method can be as an effective tool for interpreting soil and plant analysis in plant nutrition management at paddy farming systems.
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Data Availability Statement
Data is available on reasonable request from the authors.
 
Acknowledgements
The authors are thankful to the AREEO for financial supports.
 
Conflict of interest
The authors declare no conflict of interest.
 
Ethical considerations
The authors avoided data fabrication, falsification, plagiarism, and misconduct.