Analysis of Soil Nutrient (NPK) Test Value – Relative yield Relationship for Harumanis Mango using Modification Arcsine-Log Calibration Curve.

The cultivation of Harumanis mango (Mangifera indica) is of significant agricultural importance, especially in tropical regions like Malaysia, where it is renowned for its exceptional taste and quality. Maximizing mango yield and maintaining fruit quality are vital aspects of successful cultivation, relying on optimal soil nutrient management, particularly nitrogen (N), phosphorus (P), and potassium (K). In this research, the soil nutrient test value – relative yield relationship for Harumanis mango is investigated using a modification arcsine-log calibration curve. Traditional linear calibration curves may not fully capture the nonlinearities observed in crop responses, potentially leading to inaccurate nutrient requirements for optimal yield. By employing the innovative modification arcsine-log calibration curve, a more precise and robust relationship between soil nutrient test values and relative mango yield is established. Soil samples are collected from mango orchards, and NPK levels are measured using standardized laboratory techniques, alongside corresponding relative mango yields. This study advances precision agriculture by offering precise soil nutrient recommendations for mango farmers. Utilizing calibrated curves improves mango yield, minimizes nutrient waste, and encourages sustainable farming. In conclusion, the modified arcsine-log calibration curve reveals vital insights for optimal Harumanis mango production, benefiting the industry and sustainability.


Introduction
The Harumanis mango (Mangifera indica) is an esteemed and highly sought-after fruit known for its exceptional taste, aroma, and texture [1] [2].It is widely cultivated in tropical and subtropical regions, particularly in Malaysia, where it has gained international recognition for its superior quality [2] [3].The successful cultivation of Harumanis mangoes relies on various factors, including proper soil management and nutrient optimization4.Soil nutrients, particularly nitrogen (N), phosphorus (P), and potassium (K), play crucial roles in the growth and development of plants [5][6] [7].The availability and balance of these essential nutrients significantly influence crop yield and quality [5] [7].Hence, understanding the relationship between soil nutrient levels and the resulting mango yield is essential for efficient agricultural practices and sustainable mango production.
Establish this relationship, researchers and agricultural practitioners often rely on soil nutrient testing, which provides valuable insights into the nutrient status of the soil [8] [9].Traditionally, linear calibration curves have been used to correlate soil nutrient test values with crop yield [10].However, such curves may not always accurately capture the complex relationship between nutrient levels and yield due to nonlinear patterns often observed in crop responses [11].
In recent years, modification of the arcsine-log calibration curve has gained attention as an effective method to establish a more accurate and robust relationship between soil nutrient test values and relative crop yield [12][13][14].This modified curve accounts for nonlinearities commonly observed in plant responses, resulting in a more precise estimation of nutrient requirements for optimal yield [15].
The aim of this research paper is to analyze the soil nutrient test valuerelative yield relationship specifically for Harumanis mango using a modification arcsine-logcalibration curve [16] [17].By employing this innovative approach, we intend to determine the optimal nutrient levels (NPK) required for maximizing Harumanis mango yield while ensuring sustainable agricultural practices [18] [19].
The study will involve collecting soil samples from mango orchards, measuring the NPK levels using standardized laboratory techniques, and recording the corresponding relative mango yield from each site.These data will then be used to construct a modification arcsine-log calibration curve, enabling us to establish a comprehensive understanding of the nutrient-yield relationship for Harumanis mango [20].
The findings from this research will contribute to the advancement of precision agriculture by providing mango farmers and agricultural practitioners with accurate and reliable recommendations for soil nutrient management.By optimizing nutrient application based on scientifically validated calibration curves, farmers can improve mango yield, minimize nutrient wastage, and promote sustainable production practices.Investigating the soil nutrient test valuerelative yield relationship for Harumanis mango using a modification arcsine-log calibration curve holds great promise for enhancing mango cultivation practices.This research paper aims to shed light on the specific nutrient requirements necessary to maximize yield, thus enabling farmers to achieve optimal production levels and contribute to the economic growth of the mango industry.

Phenology Stage for Harumanis Mango
Mango phenology encompasses the stages of dormancy, bud break, flowering, fruit set, fruit development, ripening, harvest, and leaf fall.Mango trees transition from dormancy to produce flowers and fruits in response to warmer temperatures and longer days.The fruit matures over several months, eventually ripening and becoming ready for harvest.Factors like climate, soil, and mango variety influence the timing of each stage.Close monitoring of these stages aids farmers in optimizing cultivation practices and predicting harvest times during the mango growing season.

Soil Nutrient Data Collection:
To conduct a comprehensive data collection on Harumanis mango orchards, it is crucial to carefully select a representative orchard with a consistent production history and typical agricultural practices.For this particular data collection, we chose Muzium Mempelam Kuala Sala Alor Star as our study site, commencing from 1st May 2021 to 30th May 2022.To ensure a proper study, we obtained permission from the Head of Agriculture Department Kedah to collect soil samples.The selected orchard was divided into distinct sampling zones, taking into account factors such as tree growth, soil texture, and management practices.This zoning approach allows for a more accurate representation of the orchard's overall soil conditions.

Soil Nutrient Analysis:
Collected soil samples from the Harumanis mango orchard in Perlis, their safe transportation to Alex Steward Laboratory (M) Sdn Bhd is crucial for subsequent analysis.The chosen method for analysis is Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), following the GB/T 22923-2008 standard for Determination of Nitrogen, Phosphorus, and Potassium in Fertilizers by Auto Analyzer.These measures ensure that the samples reach the laboratory in optimal condition for accurate analysis.ICP-OES is a sensitive and versatile analytical technique that provides precise results on nitrogen, phosphorus, and potassium content in the soil samples.Comparing our system readings with those from the laboratory analysis validates the reliability and accuracy of our measurement system, reinforcing the credibility of our findings.These rigorous procedures and collaborating with Alex Steward Laboratory (M) Sdn Bhd, a reputable laboratory, we can confidently assess the nutrient composition of the Harumanis mango orchard's soil samples.This valuable information will aid in making informed decisions for fertilizer application and nutrient management strategies to enhance mango production.

Relative yield Analysis:
This research delves into the analysis of soil test values for essential nutrientsnitrogen (N), phosphorus (P), and potassium (K) -across different vegetative shoot flush stages.Leveraging the Arcsine-Log Calibration Curve (ALCC) method, we explore correlations (r) and model fit measures, including the Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC).The ALCC method, through its transformations and linearization, facilitates accurate predictions of nutrient levels required for optimal plant growth.Our research aims to provide valuable insights into nutrient management practices, assisting in informed decisions to maximize crop yield while adhering to critical nutrient thresholds.
To assess the relative yield (RY) for each zone, use the following equation: RY = (Yield of each zone / Highest average yield across all zones) × 100.This calculation helps standardize the yield data by comparing it to the zone with the highest average yield.The relative yield provides a valuable metric for evaluating the performance of each sampling zone.
To ensure accurate data collection and minimize errors, it is crucial to follow standardized procedures.Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC) for the Arcsine-Log Calibration Curve (ALCC) model, and Bayesian Information Criterion (BIC) for the same model: x Root Mean Squared Error (RMSE): The RMSE measures the average deviation of predicted values from the actual values.For a dataset with n observations, predicted values yyi and actual values xi , the RMSE is calculated as follows: x Akaike Information Criterion (AIC) for ALCC Model: The AIC is used for model selection and is a measure of the model's goodness of fit and complexity.For the ALCC model, the AIC is computed using the log-likelihood function ℒ and the number of parameters k in the model: x Bayesian Information Criterion (BIC) for ALCC Model: Similar to AIC, BIC is also used for model selection, but it penalizes complex models more strongly.The BIC is calculated using the loglikelihood function ℒ, the number of parameters k, and the number of observations n in the model: This includes using consistent measurement techniques and recording data meticulously.Adhering to standardized procedures improves the reliability and consistency of the collected data, allowing for more robust analysis and interpretation of the results.
By monitoring mango yield data, conducting multiple harvests, and calculating relative yields, researchers can gain valuable insights into the productivity of different sampling zones in the Harumanis mango orchard.These findings can guide future management decisions, such as optimizing resource allocation and implementing targeted interventions to enhance overall mango production.

Calculation of Relative yield (RY):
Step 1: RY = (Yield of each zone / Highest average yield across all zones) × 100 Step 2: Calculation of Critical STV for RY= 90 and 100:

Construction of Modification Arcsine-Log Calibration Curve:
To construct the modification arcsine-log calibration curve, the collected soil nutrient data (NPK) and corresponding relative yield data should be utilized.The goal is to determine the nonlinear relationship between soil nutrient levels and relative yield using suitable statistical methods.The construction of the calibration curve requires validation to ensure its reliability and accuracy.Statistical analysis techniques can be utilized to assess the goodness of fit of the curve to the data.These validation methods help determine the effectiveness of the calibration curve in predicting relative yield based on soil nutrient levels.

Soil Nutrient Data
Data Collection was carried out over the one seasons from 2020 to 2021, focusing on six teen-years-old Harumanis trees totaling 24 trees.These trees were planted at a spacing of 30 x 30 feet apart, utilizing surface irrigation conditions, within a government farm located in Muzium Mempelam Kuala Sala, Alor Setar, Kedah.The primary objective of this research was to determine the optimal fertilizer rate necessary to achieve a commercially viable crop yield with high-quality produce, ensuring economic feasibility for the grower.
The data collection process involved the selection of 24 trees within an area covering approximately 14,850 square feet.Six designated sampling points were identified for each tree.Data collection occurred daily from 7:00 am to 11:00 am throughout the research period, totaling 2920 data points for one season (May 2020 -May 2021).Each day, eight data samples were taken from each tree.The data sampling procedure entailed recording measurements at five-minute intervals for six consecutive samples, resulting in a 30minute duration.Subsequently, the average value of these six data points was calculated, providing a comprehensive and accurate dataset for analysis.

Soil Nutrient Data Analysis
The investigation utilized the modification arcsine-log calibration curve result in table 1, to establish the relationship between nutrient levels and mango yield, providing valuable insights for precision nutrient management at each growth stage During the first stage of vegetative shoots, nitrogen demonstrated a robust positive correlation (r=0.9614) with mango yield, emphasizing its crucial role in early vegetative growth.Phosphorus and potassium also exhibited strong positive correlations (r=0.9557 and r=0.9583, respectively), underlining their significance in supporting root and shoot development.
Moving to the second stage, nitrogen continued to exhibit a high positive correlation (r=0.9501) with mango yield, highlighting its ongoing importance in vegetative growth.Phosphorus showed a strong positive correlation (r=0.9665),suggesting its role in promoting healthy and vigorous vegetative growth.
Remarkably, potassium demonstrated an exceptionally strong positive correlation (r=0.9952),indicating its critical role in supporting robust vegetative growth and overall plant health during this stage.
In the third stage of vegetative shoots, nitrogen maintained a strong positive correlation (r=0.9340) with mango yield, reinforcing its continued significance in supporting vegetative growth.Phosphorus and potassium both displayed robust positive correlations (r=0.9636),reaffirming their roles in promoting optimal vegetative development and nutrient uptake during this final stage.
The result includes the target value of 90 percent for each nutrient (which represents the desired level of the nutrient), as well as the CSTV (Critical Soil Test Value) for each nutrient at each stage.Additionally, the data includes the Lower Limit (LL) and Upper Limit (UL) of the CSTV range (example figure 1) for each nutrient at each stage.
Here's a breakdown of the information for each nutrient and stage:

Discussion and Conclusion
The analysis of soil nutrient levels and their correlations with mango yield during different vegetative shoot stages offers valuable insights into nutrient requirements and their impact on Harumanis mango cultivation.The study establishes strong positive correlations between nitrogen, phosphorus, and potassium levels with mango yield, emphasizing their essential roles in supporting vegetative growth and enhancing productivity.The research also highlights stage-specific nutrient requirements, with higher nitrogen during early stages, and phosphorus and potassium becoming critical as the plant progresses.Potassium stands out as a key nutrient for mango production, consistently exhibiting the highest correlation coefficients.
The use of modification arcsine-log calibration curve enables precision nutrient management, promoting efficient nutrient utilization and sustainable practices.By maintaining nutrient levels above specified CSTV values, farmers can optimize yield and fruit quality while minimizing waste.These findings have significant implications for the mango industry, offering opportunities to enhance production and economic growth.Implementing stage-specific nutrient management based on validated calibration curves empowers farmers to make informed decisions, leading to increased yields and economic returns.
Future research can explore nutrient requirements during other growth stages and investigate the influence of micronutrients and environmental factors on mango yield, contributing to comprehensive nutrient management strategies.
In conclusion, precision nutrient management based on soil nutrient correlations during vegetative shoot stages is vital for optimizing mango production and fostering sustainable agriculture.Tailoring nutrient applications according to specific growth stages can lead to higher yields, improved fruit quality, and overall prosperity in the mango industry.

Table 1 :
Modification Arcsine-log Calibration Curve Result The target value for each nutrient at each stage is 90 percent, representing the desired level of the nutrient in the soil for optimal plant growth.The CSTV is the Critical Soil Test Value for each nutrient at each stage.It represents the minimum level of the nutrient required in the soil to achieve the target value of 90 percent.The Lower Limit (LL) and Upper Limit (UL) define the range around the CSTV.Soil test values falling within this range are considered acceptable for meeting the target value.Practical Application: The CSTV and its range (LL and UL) provide valuable information for nutrient management in agricultural practices.Farmers and soil scientists can use this data to determine the optimal nutrient levels in the soil to ensure healthy plant growth and maximum yield.Soil test values above the CSTV indicate sufficient nutrient levels, while values below the CSTV suggest the need for nutrient supplementation through fertilization.The CSTV range provides a margin of safety, allowing for minor fluctuations in nutrient levels without significantly affecting plant growth.