Multivariate Economic Modeling (PLS-SEM) and Pearson Correlation of B:C Ratio, Yield, and Net Income Responses to INM Treatments in Dashehari Mango under Medium Density Planting

Multivariate Economic Modeling (PLS-SEM) and Pearson Correlation of B:C Ratio, Yield, and Net Income Responses to INM Treatments in Dashehari Mango under Medium Density Planting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multivariate Economic Modeling (PLS-SEM) and Pearson Correlation of B:C Ratio, Yield, and Net Income Responses to INM Treatments in Dashehari Mango under Medium Density Planting Kuldeep, Ashok Kumar Singh, Mohammad Yaseen, Amit Bhatnagar, Omveer Singh, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9456371/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims This study aimed to evaluate the effects of integrated nutrient management (INM) strategies involving reduced recommended fertilizer doses (RDF) and different micronutrient application methods on benefit-cost (B:C) ratio, fruit yield, and net income in medium-density Dashehari mango. Methods The two-year (2023–2024) experiment was conducted in a 13-year-old Dashehari mango orchard at Pantnagar, Uttarakhand, using a factorial randomized block design with ten treatments and three replications. Treatments included 100, 75, 50, and 25% RDF combined with soil and/or foliar applications of micronutrients. Economic parameters (cost of cultivation, gross income, net income, B:C ratio) and yield were recorded and analyzed using two-way ANOVA, Pearson correlation, and PLS-SEM. Results The combination of 75% RDF plus two foliar micronutrient sprays (no soil application) recorded the highest B:C ratio of 2.96, maximum fruit yield (22.68 t ha⁻¹), and highest net income (508.88 × 10³ Rs. ha⁻¹). This treatment outperformed the 100% RDF control (B:C 1.84). Strong positive correlation was observed between B:C ratio and net income (r = 0.868, P < 0.001). PLS-SEM showed B:C ratio had a significant positive effect on net income (β = 0.560, p = 0.002) and explained 98.9% of its variance. Group culture and quality of experience positively moderated the B:C ratio to yield a relationship. Conclusion Reduction of RDF to 75% combined with two foliar micronutrient sprays without soil application proved most profitable and sustainable for Dashehari mango cultivation under medium density planting by enhancing economic returns and nutrient use efficiency while reducing fertilizer dependency. Horticulture Benefit Cost Ratio (B:C Ratio) Net Income Mango RDF PLS-SEM Correlation Figures Figure 1 Figure 2 Introduction Mango ( Mangifera indica L.) is one of the top tropical fruit crops of global importance, which is appreciated for its nutritional profile, sensory attributes, and contribution for the rural economic and food security of tropical and subtropical areas (Srivastava, 2021 ; Hasan et al., 2023). As a perennial orchard, mango production is a long-term investment that creates job opportunities as well as export earnings and the enhancement of agro-biodiversity. The world’s mango production is dominated by India, which accounts for 40–45% of the world’s mango output with an annual capacity above 22 million tonnes, from approximately 2.6 million hectares (Balaganesh et al., 2023 ; NHB, 2024). In turn, commercial intensive cultivation has caused escalated input costs, decreasing nutrient utilization efficiency, erosion of the soil, and diminished profit potential, most especially in high-input systems (Adak et al., 2022 ; Umar et al., 2022 ). These challenges highlighted the importance of adaptive nutrient patterns to modern orchard systems. Dashehari, an early-maturing cultivar known for its flavor and market demand, accounts for the overwhelming majority of cultivation in the subtropical Tarai region of northern India (Kuldeep et al., 2025; Singh et al., 2015 ). Conventional (low density) planting (10 × 10 m; ~100 trees ha⁻¹) has been replaced over time with medium density (5 × 5 m; 400 trees ha⁻¹), leading to 20–50% improvement in land utilization and per hectare productivity (Oosthuyse, 2009 ; Dutta et al., 2019 ; Ram et al., 2021 ). Such high-capacity systems can generate an early stage of bearing and enhance canopy management, but the need for careful nutrition to prevent shading, nutrient depletion, and alternate bearing (Menzel, 2017 ; Gaikwad et al., 2017 ). 5 × 5 m spacing is proven in Dashehari orchards to lead to a high increase in yield characteristics with full fertilization as well, yet maximum recommended fertilizer (RDF = 1000 g N + 750 g P₂O₅ + 1000 g K₂O + 50 kg FYM tree⁻¹ year⁻¹) is charged in addition of economic costs and environmental detriment (Biswas, 2011 ; Malihabad region trials, 2025). Integrated Nutrient Management (INM) that uses reduced RDF in combination with manures, plant organic manure, and soil- or foliar-targeted micronutrients to optimize nutrient availability, revive soil microbial activity, and retain fruit quality without loss of yield (Srivastava, 2021 ; Vikalp et al., 2022; Harsh et al., 2025 ). In perennial fruit crops like mango, INM is associated with improved root proliferation, dehydrogenase, urease, phosphatase enzyme, arbuscular mycorrhizal fungi (AMF) spore density, and leaf chlorophyll content, and decreased nutrient loss by fixation and leaching (Mishra et al., 2019 ; Ali et al., 2024 ; Kheir et al., 2021 ). Two-year experiment carried out to obtain this effect in two-year field studies, Dashehari mango in medium-density-planting environment, has been performed, revealing that between 75%-50% RDF supplemented with foliar micronutrient sprays (Fe, Zn, B, Ca) enhances post-harvest soil quality status (Kuldeep et al., 2025; Umar et al., 2022 ; El-Salhy et al., 2025 ), leading to 15–30% higher fruit yield. The use of INM also enhances favorable plant-microbe feedback and climate-resilient production practices in subtropical orchards (Srivastava & Singh, 2008 ; Harsh et al., 2025 ). Financial justification still represents the determining factor in the uptake of any nutrient management practice by small and marginal mango producers (Adnan et al., 2025 ; Ndiwa et al., 2024 ). Benefit-cost analysis can offer an alternative quantitative metric for determining the profitability of food production by comparing overall gross income from yield to total cultivation cost and plant fertilizers, micronutrients used with their work, labour, and fixed operations (Gupta et al., 2022; Pant et al., 2023 ). The B:C ratios achieved in mango-growing fruit studies published recently show B:C values 1.84 under 100% RDF control to 2.96 under 75% RDF + two foliar micronutrient sprays (without soil application). With lower fertilizer prices, higher marketable fruit yields (up to 22.68 t ha⁻¹), and premium fruit quality, which can fetch better prices (Kuldeep et al., 2025; Devi, 2021 ; Singh and Banik, 2011 ). In soil-applied micronutrient treatments, the B:C ratio tends to decline, often resulting from increased input costs but no corresponding increase in yield (Bana et al., 2022 ; Dass et al., 2022 ). Net income and B:C ratio is highly positively correlated with yield, indicating that careful RDF reduction accompanied by focused foliar nutrition will yield maximum profitability while reducing dependence on chemical fertilizer (Kumar et al., 2022 ; Gourkhede et al., 2026 ). Pearson correlation and Partial Least Squares Structural Equation Modelling (PLS-SEM) are reliable multivariate methods to unify complex interrelationships between nutrient management practices, soil-plant nutrient pockets, yield, and economic parameters (Tama et al., 2023 ; Şengül et al., 2025 ). The Pearson correlation is used to identify linear relationships (B ratio and net income, r = 0.868, P < 0.001), and PLS-SEM is used to simultaneously estimate direct, indirect, moderating, and effect in two or more latent variables (Hair et al., 2021 ; Bunkus et al., 2020 ). Such methods have been very successfully used in agricultural economics for modeling farmer adoption behavior, risk perception, sustainability attributes, and nutrient-use productivity in perennial and conservation agriculture systems (Aziz et al., 2020 ; Bouyghrissi et al., 2025 ). Unlike standard ANOVA, PLS-SEM provides causal pathways (eg, B:C ratio → net income) and moderator variables, and ultimately generates evidence-informed policy suggestions for resource-limited tropical agroecosystems (Paramesh et al., 2023 ; Cen et al., 2020 ). Notwithstanding much agronomic expertise on INM in mango, multivariate economic modelling of B:C ratio, yield, and net income responses to medium-density planting is still rare (Srivastava, 2021 ; Lingwan et al., 2025 ; Jayasinghege et al., 2024 ). The majority of the presented studies use non-experimental analysis of yield or soil health measures and miss causal pathways between the reduced RDF + foliar micronutrients and the economic impact of PLS-SEM (Ravikiran, 2018; Ahmad et al., 2018; Shagun et al., 2024). Cultivar-specific (Dashehari) and location-specific (Tarai region) data for 75% RDF + two foliar sprays without soil micronutrients are especially limited, especially for progressive fertilizer reduction regimes (100 − 25% RDF) (Oosthuyse, 2009 ; Singh et al., 2017). This study aims to fill these gaps by studying the direct, indirect, and moderating effects of ten INM treatments on economic indicators using Pearson correlation and PLS-SEM analysis in a 13-year-old medium-density Dashehari mango orchard. The results are expected to offer sustainable, economical, and evidence-based nutrient management guidelines for tropical perennial agroecosystems to promote acceptance of various INM approaches among mango farmers. Materials and Methods Orchard site and experimental period The study was conducted during 2023–2024 at the Horticulture Research Centre, Patharchatta, GBPUA&T, Pantnagar, Uttarakhand (≈ 29.5°N, 79.3°E; 244 m a.s.l.; Fig. 1 ). The experiment used a medium-density mango ( Mangifera indica L. cv. Dashehari) orchard of uniform 13-year-old trees planted at 5 × 5 m spacing (400 trees ha⁻¹). Soil and climate The site lies in the Tarai agro-ecological zone with a sub-humid subtropical climate. The experimental soil is silty clay loam (Patharchatta Series II, Mollisol). Experimental design and treatments The experiment was laid out in a randomized block design (FRBD) with 10 treatments and three replications. Treatments evaluated combinations of full, reduced (75%, 50%, 25%) recommended fertilizer dose (RDF = 1000 g N + 750 g P₂O₅ + 1000 g K₂O + 50 kg FYM tree⁻¹ year⁻¹) and soil-applied micronutrients (Fe, Cu, Zn each 100 g tree⁻¹ and B 50 g tree⁻¹), together with one or two foliar sprays of micronutrients. In brief treatment details: T 1 (Control): 100% RDF applied in the basin after harvest. T 2 -T 4 : 75%, 50%, 25% RDF, respectively, + soil micronutrients (Fe, Cu, Zn @100 g each + B @50 g tree⁻¹) + one foliar spray (Fe, Ca, Zn each 0.50% + B- 0.10%) (just before flowering and marble stages as a single event). T 5 -T 7 : 75%, 50%, 25% RDF, respectively, + soil micronutrients (as above) + two foliar sprays (same foliar composition; applied just before flowering and at marble stage). T 8 -T 10 : 75%, 50%, 25% RDF, respectively, without soil micronutrients but with two foliar sprays (just before flowering and at marble stage). Orchard management and fertilizer application Basins (radius: 1.5 m from trunk) were prepared around each tree. Farmyard manure was applied in October. P, K, and micronutrient basal doses were incorporated into the basins in November. Nitrogen was applied in two equal splits: first immediately after flowering and the second at the mustard-to-pea stage of fruit development. Foliar sprays (10 L water tree⁻¹ per spray) were prepared by dissolving micronutrient salts and applied with a tractor sprayer at the timings (just before flowering and at marble stage). Standard cultural practices (irrigation, weeding, pruning, pest and disease control) were followed uniformly across treatments. Economic Analysis Fertilizer cost and fixed operational costs incurred per hectare in the mango orchard during the two-year experimental period, as presented in Supplementary Tables 1 & 2a. Gross income Gross income was calculated as the total revenue obtained from the sale of mango fruits produced per hectare under each treatment Supplementary Table 2b. $$\:\text{G}\text{r}\text{o}\text{s}\text{s}\:\text{i}\text{n}\text{c}\text{o}\text{m}\text{e}=\text{Y}\text{i}\text{e}\text{l}\text{d}\:\left(\text{R}\text{s}./\text{h}\text{a}\right)\times\:\text{M}\text{a}\text{r}\text{k}\text{e}\text{t}\:\text{R}\text{a}\text{t}\text{e}\:(\text{R}\text{s}./\text{k}\text{g})$$ Total cost of cultivation The total cost of cultivation included the sum of all costs incurred for mango cultivation under each treatment, such as RDF cost, micronutrient cost, and fixed costs. $$\:\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{c}\text{o}\text{s}\text{t}\:\text{o}\text{f}\:\text{c}\text{u}\text{l}\text{t}\text{i}\text{v}\text{a}\text{t}\text{i}\text{o}\text{n}\:\left(\text{R}\text{s}./\text{h}\text{a}\right)=\text{R}\text{D}\text{F}\:\text{c}\text{o}\text{s}\text{t}+\text{M}\text{i}\text{c}\text{r}\text{o}\text{n}\text{u}\text{t}\text{r}\text{i}\text{e}\text{n}\text{t}\:\text{c}\text{o}\text{s}\text{t}+\text{F}\text{i}\text{x}\text{e}\text{d}\:\text{c}\text{o}\text{s}\text{t}\:$$ Net income Net income represented the profit obtained per hectare after deducting the total cost of cultivation from gross income. $$\:\text{N}\text{e}\text{t}\:\text{i}\text{n}\text{c}\text{o}\text{m}\text{e}\:\left(\text{R}\text{s}./\text{h}\text{a}\right)=\text{G}\text{r}\text{o}\text{s}\text{s}\:\text{i}\text{n}\text{c}\text{o}\text{m}\text{e}-\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{c}\text{o}\text{s}\text{t}\:\text{o}\text{f}\:\text{c}\text{u}\text{l}\text{t}\text{i}\text{v}\text{a}\text{t}\text{i}\text{o}\text{n}$$ Benefit-Cost Ratio The benefit-cost ratio was determined to assess the economic efficiency of each treatment, showing the returns per rupee invested. $$\:\text{B}\text{e}\text{n}\text{e}\text{f}\text{i}\text{t}\:\text{c}\text{o}\text{s}\text{t}\:\text{r}\text{a}\text{t}\text{i}\text{o}=\frac{(\text{G}\text{r}\text{o}\text{s}\text{s}\:\text{i}\text{n}\text{c}\text{o}\text{m}\text{e}-\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{c}\text{o}\text{s}\text{t}\:\text{o}\text{f}\:\text{c}\text{u}\text{l}\text{t}\text{i}\text{v}\text{a}\text{t}\text{i}\text{o}\text{n})}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{c}\text{o}\text{s}\text{t}\:\text{o}\text{f}\:\text{c}\text{u}\text{l}\text{t}\text{i}\text{v}\text{a}\text{t}\text{i}\text{o}\text{n}}$$ Yield (t ha − 1 ) The yield from each tree was multiplied by the total number of trees planted per hectare, and the result was expressed in t/ha. Statistical Analysis The experimental findings were analyzed using two-way ANOVA to determine the significant effects of recommended dose of fertilizers (RDF) levels and micronutrient application methods, as well as their interaction, on the benefit-cost (B:C) ratio, fruit yield, and net income. All parameters of interest were analyzed (N = 30) using descriptive statistics (mean ± standard deviation). Pearson’s correlation test was conducted to investigate the relationships between fruit yield, net income, B:C ratio, and RDF level with IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA). Partial least squares structural equation modeling (PLS-SEM) was performed in order to demonstrate the direct, indirect, and moderating effects of B:C ratio on RDF level, yield, and net income with group culture (GC) and quality of experience (QE) as potential moderators. The model was estimated based on the path-weighting scheme on standardized data (maximum 3000 iterations, stopping criterion 10⁻⁷). Path coefficients, direct effects, indirect effects, and moderation effects were tested by bootstrapping with 5000 resamples using the percentile method. All statistical tests were considered significant at *p* ≤ 0.05 in SmartPLS (version 4.1.1.5). Results Cost of cultivation The costs in operation of the mango orchard were classified as variable fertilizer costs (macronutrients NPK and FYM, and micronutrients) and fixed operation costs. All costs are per hectare over the two-year experimental period. Costs for macronutrients (urea, single superphosphate [SSP], muriate of potash [MOP], and farmyard manure [FYM]) were based on the recommended amounts of fertilizers (RDF), and scaled proportionally among treatments. Micronutrient costs appeared to differ according to the quantity, source, and method of application (soil applied plus the foliar sprays), reflecting the nature of each group treated. Supplementary Table 1 shows the overall fertilizer cost of the ten treatments (T 1 -T 10 ). Treatment T 1 (100% RDF) produced macronutrient total costs. Treatments T 2 -T 4 received 75%, 50%, and 25% RDF, respectively, with standard micronutrient packages. Treatments T 5 -T 7 were given the same RDF dosage, but the micronutrient contents were increased (elevated rates of Fe, Zn, B, and Ca). Treatments T 8 -T 10 received similar levels for the RDF, with a reduced micronutrient package (Fe, Zn, B, and Ca only; Cu omitted and lower overall quantities). Fixed operational costs and Gross income were comparable for all treatments (Supplementary Table 2a & 2b). Benefit-Cost (B:C) Ratio, Yield, and Net Income The Benefit-Cost (B:C) ratio was also influenced strongly by the appropriate fertilizer doses recommended (RDF) and the type of micronutrient applied (Table 1 ). In all treatments, the B:C ratios ranged from 0.32 to 2.96, over 1.0 for profitable returns. B:C increased most under 25% RDF if the micronutrient application in two foliar sprays only (1.65), and soil micronutrient application in combination with a single or two foliar sprays was markedly lower (0.45 and 0.32, respectively). The same trend was also noted in 50% RDF, with two foliar sprays alone yielding the highest B:C (2.77) ratio, compared to 0.79 and 0.72 for soil and foliar treatments. At 75% RDF, a maximum B:C ratio at 2.96 was obtained through two foliar sprays of micronutrients alone, which was significantly better compared with both soil-plus-single-foliar (0.90) and soil plus two-foliar (1.13) combinations. The B:C ratio of 100% RDF control (no micronutrients) was 1.84. When averaged across the 25%, 50% and 75% RDF levels, the main effect of micronutrient application method showed that only two foliar sprays produced the highest mean B:C ratio (2.46 ± 0.71), which was more than three times greater than the ratios with soil micronutrients plus single foliar spray (0.71 ± 0.23) or soil micronutrients plus two foliar sprays (0.72 ± 0.41). The 100% RDF control without micronutrients had a B:C of 1.84. Overall, the highest B:C ratio recorded in the entire experiment was 2.96 under the treatment combination of 75% RDF + two foliar sprays of micronutrients. The highest fruit yield (22.68 t ha⁻¹) and net income (508.88 × 10³ Rs. ha⁻¹) were obtained with this treatment compared to the combination used in our study. Whereas, the soil-containing micronutrients consistently resulted in the B:C ratio decline below 100% at every RDF level, and the difference was due mainly to higher cost, but less fruit yield, and marginal return. Therefore, strategic reduction of RDF to 75% alongside two foliar sprays of micronutrients (without soil application) maximized the economic benefit of the method by yielding a superior B:C ratio over both full-dose fertilization and any soil-plus-foliar regime. Table 1 Effect of RDF levels and micronutrient application methods on yield, net income, and benefit-cost (B:C) ratio, mean of two years of data. RDF Level Micronutrients Regime Yield (t ha- 1 ) Net Income (10 3 Rs. /ha) B: C Ratio 25% Soil Micronutrients + Single Foliar Spray 16.28 100.70 0.45 Soil Micronutrients + Two Foliar Sprays 17.17 82.30 0.32 Two Foliar Sprays 16.60 206.57 1.65 Total 16.68 ± 0.45 129.86 ± 67.07 0.81 ± 0.73 50% Soil Micronutrients + Single Foliar Spray 17.75 195.62 0.79 Soil Micronutrients + Two Foliar Sprays 19.60 205.76 0.72 Two Foliar Sprays 18.66 411.42 2.77 Total 18.67 ± 0.93 270.93 ± 121.77 1.43 ± 1.16 75% Soil Micronutrients + Single Foliar Spray 20.57 243.09 0.90 Soil Micronutrients + Two Foliar Sprays 21.81 347.07 1.13 Two Foliar Sprays 22.68 508.88 2.96 Total 21.69 ± 1.06 366.34 ± 133.94 1.66 ± 1.13 100% Control (No Micronutrients) 17.39 225.28 1.84 Total 17.39 225.28 1.84 The Main effect of the micronutrient’s application method 100% RDF (Without soil and foliar spray of micronutrients) 17.39 225.28 1.84 25, 50 and 75% RDF + Soil micronutrients + Single foliar spray of micronutrients 18.20 ± 2.18 179.80 ± 72.50 0.71 ± 0.23 25, 50 and 75% RDF + Soil micronutrients + two foliar sprays of micronutrients 19.53 ± 2.32 211.71 ± 132.48 0.72 ± 0.41 25, 50 and 75% RDF + two foliar sprays of micronutrients 19.31 ± 3.09 375.62 ± 154.30 2.46 ± 0.71 Total 18.85 ± 2.23 252.67 ± 133.46 1.35 ± 0.93 Descriptive Statistics and Pearson Correlation Analysis Descriptive statistics of study variables (N = 30) are shown in Table 2 . Mean (± standard deviation) values: Yield = 18.85 ± 2.20, Net Income = 252.62 ± 129.30, B:C ratio = 1.35 ± 0.90, and RDF level = 55.00 ± 24.91. Pearson correlation analysis ( Table 3 ) showed a statistically significant positive relationship between the B:C ratio and RDF level (r = 0.410, P = 0.025). The result demonstrates that higher RDF levels are associated with higher C:B (Cost-Benefit) ratios for this dataset. Nevertheless, lowering the RDF is assumed to lower the expenditure on the cultivation phase considerably, mainly through reduced agricultural fertilizer inputs. Cutting cultivation costs can also increase the benefit-cost (B:C) ratio, indicating that prudently lowering RDF levels can improve the overall economic efficiency, as long as yield decreases are minimal. The B:C ratio also reported strong, positive correlations with Net Income (r = 0.868, P < 0.001) and moderate positive correlations with Yield (r = 0.395, P = 0.031). All these correlations were significant at the level of significance of 0.01 or 0.05 (two-tailed test). Table 2 Descriptive statistics (Mean, Std. Deviation, N) for all four variables (Yield, Net Income, B:C Ratio and RDF Level). Yield (t/ha) Nat Income (Rs.) B:C Ratio RDF Level Yield (t/ha) Pearson Correlation 1 0.77** 0.39* 0.57** Sig. (2-tailed) 0.001 0.001 0.031 0.001 N 30 30 30 30 Net Income Pearson Correlation 0.77** 1 0.87** 0.53** Sig. (2-tailed) 0.001 0.001 0.001 0.003 N 30 30 30 30 B:C Ratio Pearson Correlation 0.39* 0.87** 1 0.41* Sig. (2-tailed) 0.03 0.001 0.001 0.025 N 30 30 30 30 RDF Level Pearson Correlation 0.57** 0.53** 0.41* 1 Sig. (2-tailed) 0.001 0.003 0.025 0.001 N 30 30 30 30 Table 3 Pearson correlation matrix Yield (t/ha) Mean Std. Deviation 18.85 2.19 Net Income 252.62 129.30 B:c Ratio 1.35 0.90 RDF level 55.00 24.92 Partial Least Squares Structural Equation Modelling (PLS-SEM: Path Coefficient) Benefits-to-cost (B:C) Ratio has a direct impact on RDF Level, Yield, Net Income, which were estimated using partial least squares structural equation modelling (PLS-SEM) method, and group culture (GC) and QE were proposed as potential moderators. Model estimation was implemented based on the method of path-weighting with the standardized data (the maximum 3000 iterations, stopping point 10⁻⁷; full settings in Supplementary Tables S3-S4 ). The structural model (Fig. 2 ) has accounted for high variability among endogenous constructs (R² = 0.421 for Yield, R² = 0.548 for RDF Level and R² = 0.989 for Net Income). In Table 4 , direct standardized path coefficients, bootstrap means, standard deviations, t-statistics, and p-values (5000 resamples, percentile method) are described. The B:C Ratio was our main predictor, having two statistically significant direct associations. It had a significant direct positive influence on Net Income (β = 0.560, t = 3.154, p = 0.002), and a significant negative direct impact on Yield with (β = −2.309, t = 2.147, p = 0.032). The inverse path from B:C Ratio to RDF Level was not significant, although (β = 0.266, t = 0.211, p = 0.833). Moderation of significant effect by the B:C Ratio was also observed as well. GC (B:C Ratio → Yield) was a positive effect on Yield (β = 2.644, t = 2.703, p = 0.007), and QE (B:C Ratio) a positive moderating effect on Yield (β = 0.590, t = 2.454, p = 0.014). All the additional paths were non-significant - RDF Level as an end-point or mediator and other direct paths included were non-significant (p > 0.05; detailed statistics in Table 1 ). Indirect effects total and specific to the B:C Ratio were also non-significant (p > 0.05). Indirect effect of B:C Ratio on Net Income was − 0.742 (95% CI − 5.425 to 2.804, p = 0.731), and no specific indirect pathway, mediated by RDF Level or Yield, reaches statistical significance (Supplementary Tables S5-S8). Bootstrap distributions ( Supplementary Figs. S1-S3) established the robustness of such estimates by finding approximately normal distributions which were found to be centered in the vicinity of the sample mean and were relatively non-bias biased. These results show that the B:C Ratio positively drives Net Income, negatively drives Yield with GC and QE representing major moderation of the relationship between B:C Ratio → Yield. Table 4 Direct path coefficients (standardized) - Mean, STDEV, T-values, p-values Path Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics P values B:C Ratio → Net Income 0.56 0.58 0.18 3.15 0.00 B:C Ratio → RDF Level 0.27 0.44 1.26 0.21 0.83 B:C Ratio → Yield -2.31 -1.67 1.08 2.15 0.03 GC (B:C Ratio → Net Income) 0.16 0.15 0.24 0.65 0.51 GC (B:C Ratio → RDF Level) -0.59 -0.81 1.28 0.46 0.65 GC (B:C Ratio → Yield) → Yield 2.64 2.00 0.98 2.70 0.01 GC (RDF Level → Net Income) 0.20 0.23 0.55 0.37 0.71 GC (Yield → Net Income) -0.16 -0.15 0.27 0.60 0.55 GC (Yield → RDF Level) → -1.48 -0.89 1.19 1.24 0.22 QE (B:C Ratio) → RDF Level 0.42 0.40 0.50 0.84 0.40 QE (B:C Ratio) → Yield 0.59 0.40 0.24 2.45 0.01 QE (RDF Level) → Net Income -0.21 -0.24 0.13 1.55 0.12 QE (Yield) → Net Income 0.03 0.04 0.09 0.36 0.72 RDF Level → Net Income -0.25 -0.29 0.58 0.42 0.67 Yield → Net Income 0.49 0.45 0.27 1.81 0.07 Yield → RDF Level 0.78 0.25 1.06 0.74 0.46 Discussion Economic Efficiency of Reduced RDF Combined with Foliar Micronutrients The B:C (benefit-cost) ratio and micronutrient doses depend on RDF levels and the application process. The maximum B:C ratio (2.96) was obtained with 75% RDF (two foliar sprays of micronutrients & no soil application of micronutrients). This was also the treatment that gave the highest yield of fruit (22.68 t ha⁻¹) and generated the highest net incomes (508.88 × 10³ Rs. ha⁻¹). Simultaneously, soil micronutrient content-based and foliar sprays also induced B:C ratios (0.32–1.13) that were significantly lower at all lower RDF concentrations (25–75%), resulting in higher input costs, no yield gain, and no return. A moderate B:C ratio of 1.84 was achieved for the 100% RDF control (no micronutrients). This establishes the economic merits of the reduction of RDF to 75% supplemented with foliar micronutrient application. In terms of efficiency and productivity, foliar sprays allow for rapid leaf processing without soil fixation, leaching, and immobilization loss. Planting the soil provided additional micronutrient input, increasing its cost and reducing agricultural utility, where the average B:C ratio for the two foliar sprays, only (2.46 ± 0.71) in the treated low quantity of RDF, was significantly greater than the soil plus foliar results. This is in line with the findings of Bana et al. ( 2022 ), 75–100% RDF foliar micronutrient-embedded NPK resulted in substantially better eggplant yield, biofortification, and economy. Similarly, Dass et al. ( 2022 ) found that foliar macro- and micronutrients of soybean at lower RDF levels result in an increased soybean seed yield (18.5–37.8%), net returns (21–58%), and B:C ratio. Ahmed et al. (2024) and other studies concluded that foliar application results in a better recovery of nutrients and economic returns than soils, especially considering low RDF, since fixation leads to horticulture soil pH. Niu et al. (2020) found chelated foliar micronutrients reduced overall fertilizer input by 15–30% at targeted delivery stages during growth and increased yield and B:C ratio. These findings are supported by recent mango-specific studies: 75% RDF in combination with two foliar micronutrient sprays had the highest B:C ratio, higher fruit yield, and net returns in mango cv. Dashehari under medium density planting (Kuldeep et al., 2025) and foliar micronutrient applications (in the absence or presence of reduced RDF) considerably increased the yield, quality, and economic efficiency of other mangoes over soil-only methods (Umar et al., 2022 ). Overall, this information supports the principle of integrated nutrient management (INM), implying that for sustainable horticultural production, 75% RDF and two foliar micronutrient sprays - without soil - produce optimized economic returns and decrease the amount of chemical fertilizer required. Correlation among Yield, Net Income, and B:C Ratio Pearson correlation analysis indicated significant positive relationships between fruit yield, net income, B:C ratio, and the recommended dose of fertilizers (RDF) level. It was found that the fruit yield was in positive relations and relatively consistent with the net income (r = 0.77, P = 0.001) and moderate with the RDF level (r = 0.57, P = 0.001) and the B:C ratio (r = 0.39, P = 0.031). Net income was very strongly associated with B:C ratio (r = 0.87, P < 0.001), and the RDF level (r = 0.53, P = 0.003) was also moderately associated. The B:C ratio did moderately show a positive relationship with RDF (r = 0.41, P = 0.025). These associations suggest that both productivity and economic gain were associated with optimum RDF levels in general. The highest B:C ratio (2.96) and maximum fruit yield (22.68 t ha⁻¹), however, were obtained at 75% RDF supplemented by two foliar micronutrient sprays (without soil application). This highlights the capacity of strategic reductions of RDF with high-precision foliar delivery to more effectively achieve economic returns than application when taken in full doses in achieving optimal nutrient-use efficiency with lower input costs. These relationships correspond to integrated nutrient management (INM) principles and are in line with previous studies. Bana et al. ( 2022 ) and Dass et al. ( 2022 ) reported significant positive correlations between yield, net returns, and B:C ratio under foliar micronutrient supplementation at reduced RDF levels in eggplant and soybean, respectively. This is consistent with other work by Rathore et al. ( 2021 ) in tomato. In mango, the foliar micronutrient applications (with or without reduced RDF) have also increased both yield and quality parameters and economic returns by means of enhanced nutrient recovery (Umar et al., 2022 ). Recent research specific to mango has corroborated this finding showing that 75% RDF + two foliar micronutrient sprays can optimize associations among yield, net income, and B:C ratio while maximizing economic efficiency under medium-density planting (Kuldeep et al., 2025). PLS-SEM Path Coefficient and Moderation By means of partial least squares structural equation modelling (PLS-SEM), we showed significant significance for causative relationships among benefit-cost (B:C) ratio, fruit yield, net income and recommended dose of fertilizers (RDF) level. The model had high explanatory power and explained 42.1% variability in fruit yield, 54.8% variance in RDF level, and 98.9% variance in net income. The B:C ratio exerted significant direct positive effect on net income (β = 0.560, t = 3.154, p = 0.002), indicating that economic efficiency is a main basis of profitability in integrated nutrient management (INM) systems (Dass et al., 2022 ; Paramesh et al., 2023 ). In contrast, the B:C ratio had a direct, negative impact on fruit yield (β = −2.309, t = 2.147, p = 0.032). This inverse correlation indicates the cost-savings obtained in the study, where the highest B:C ratio (2.96) as well as the highest fruit yield (22.68 t ha⁻¹) were achieved in concomitant 75% RDF and two foliar micronutrient sprays with no soil application. This treatment provided better net income with significantly lower input prices than full RDF systems in the present study (Bana et al., 2022 ; Cen et al., 2020 ). The linear relationship from B:C ratio to RDF level was not significant (β = 0.266, p = 0.833), and indirect impact too were non-significant (p > 0.05). The combination of these PLS-SEM findings supports the previous analysis using ANOVA and Pearson correlation by presenting positive effects of the B:C ratio on net income and revealing important trade-off effects of fine-tuned foliar nutrition. The results corroborate with previous INM study where low RDF content in conjunction with foliar micronutrient application enhances nutrient use efficiency and sustains high productivity, leading to positive economic returns in general without yield penalties (Kumar et al., 2022 ; Gourkhede et al., 2026 ). The results of a study of mango by the same PLS-SEM technique with respect to cv. Dashehari at medium density planting showed that plant nutrient status (which significantly correlated with 75% RDF + foliar sprays) was the most important driver of fruit yield, where the same treatment would result in the highest B:C ratio and economic performance (Kuldeep et al., 2025). Combined, these findings indicate that 75% RDF + two foliar micronutrient sprays (without soil application) represent the most economical and environmentally friendly choice of mango production. Conclusion This study shows that integrated nutrient management (INM) implemented for medium-density Dashehari mango orchards can be greatly enhanced by prudently lowering the recommended amount of fertilizer (RDF) and supplementing it exclusively with foliar micronutrient sprays without soil application. These treatment types produced the greatest, highest B:C ratio in terms of total fruit yield and maximum net income in contrast to the full RDF control and every treatment with a soil-applied micronutrient. Soil-plus-foliar regimes always reduced economic returns, resulting in increased input costs without increases in yield or productivity. Pearson correlation analysis demonstrated a significant correlation between B:C ratio, net income, and fruit yield. Partial least squares structural equation modelling (PLS-SEM) showed that: B:C ratio significantly positively affects net income; group culture and quality of experience positively moderate B:C-yield link. These results demonstrate that accurate delivery of foliar micronutrients with lower RDF improves the nutrient utilization efficiency and profits while avoiding reliance on chemical fertilizer treatment for maintaining high productivity. The method describes the development of an alternative, sustainable, and economically viable approach to Dashehari mango cultivation, in the context of medium-density planting systems in subtropical habitats. This INM protocol should be validated in other cultivars, in other soils, and during extended growth cycles in future studies, which in turn can support more extensive utilization in tropical horticulture. Declarations Acknowledgement Acknowledgment is extended for the financial assistance and infrastructural support provided by the Horticulture Research Centre, Patharchatta, and the Department of Horticulture, GBPUA&T, Pantnagar, Uttarakhand, India. Funding This research received no external funding Data availability statements The original contributions made in the study are contained in the article/supplementary materials. Additional question can be referred to the relevant authors. Author Contribution Statement Conceptualization, investigation and original draft writing done by Kuldeep, & AKS. Methodology and statistical analysis were done by AB, OS. Review, editing and supervision were done by SPP, SCS, HCJ, MY & MKB. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics statement This study involved field experimentation on mango (Mangifera Indica L.) plants and did not involve human participants or animals. Therefore, formal ethical approval was not required. All experimental procedures were conducted following standard agronomic research practices and the institutional guidelines of Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India. Declaration of AI-assisted writing The authors exclusively used OpenAI's ChatGPT (GPT-5 model) to improve the clarity, grammar, and style of the manuscript. The authors carefully reviewed, edited, and approved all AI-generated suggestions and take full responsibility for the final content of the paper. References Adak T, Kumar A, Singh R, Singh V (2022) Response of Dashehari mango to different zinc levels. Trop Plant Res 6(1):005. https://doi.org/10.22271/tpr.2019.v6.i1.005 Adnan N, Khan M, Ali S (2025) Smart fertilizer technology adoption. Agric Food Secur. https://doi.org/10.1186/s40066-025-00529-0 Ali A, Khan M, Ahmad S, Shah A (2024) Integrated nutrient management in mango. 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Sci Rep 11(1):18664. https://doi.org/10.1038/s41598-021-97991-6 Şengül Z, Yılmaz A, Kaya M (2025) Risk perception using PLS-SEM in pistachio production. Humanit Social Sci Commun. https://doi.org/10.1057/s41599-025-04983-w Singh AK, Sharma R, Singh V, Singh R (2015) Standardization of planting systems in mango. CABI. https://doi.org/10.5555/20153268590 Singh SR, Banik BC (2011) Integrated nutrient management in mango cv. Amrapali. Indian J Hortic 69(2):151–155 Srivastava AK (2021) Integrated soil fertility management in fruit crops. Int J Fruit Sci 21(1):1–28. https://doi.org/10.1080/15538362.2021.1895034 Srivastava AK, Singh S (2008) Plant–microbe feedbacks in mango. Acta Hort Tama RAZ, Aziz T, Rahman M (2023) Application of PLS-SEM in conservation agriculture. Agriculture 13(2):503. https://doi.org/10.3390/agriculture13020503 Umar U, Abbas W, Shahzad T, Abbas A, Fatima M, Zafar I, Ahmad S, Khan MA, Ali MA, Khan MA (2022) Micronutrients foliar and drench application mitigate mango sudden decline disorder and impact fruit yield. Agronomy 12(10):2449. https://doi.org/10.3390/agronomy12102449 Vikalp A, Arjoo, Sharma S (2022) Integrated nutrient management in fruit production. J Agricultural Res Technol. https://doi.org/10.56228/JART.2022.SP108 Result, Table Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9456371","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625436908,"identity":"1cde624e-c93c-444b-a419-4362172c5410","order_by":0,"name":"Kuldeep","email":"","orcid":"https://orcid.org/0009-0002-1248-4535","institution":"Department of Horticulture, College of Agriculture, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Udham Singh Nagar, 263145, Uttarakhand, 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09:05:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28515,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material of Original Table\u003c/p\u003e","description":"","filename":"SupplementryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-9456371/v1/5f987c77ac86199435bc9d9b.docx"},{"id":107489528,"identity":"5891b547-3146-4cb9-9c1e-34157a61dcc7","added_by":"auto","created_at":"2026-04-22 02:47:58","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":238503,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"GraphicalAbstract4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9456371/v1/dd4fd250ef920deef5e3b100.jpg"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMultivariate Economic Modeling (PLS-SEM) and Pearson Correlation of B:C Ratio, Yield, and Net Income Responses to INM Treatments in Dashehari Mango under Medium Density Planting\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) is one of the top tropical fruit crops of global importance, which is appreciated for its nutritional profile, sensory attributes, and contribution for the rural economic and food security of tropical and subtropical areas (Srivastava, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hasan et al., 2023). As a perennial orchard, mango production is a long-term investment that creates job opportunities as well as export earnings and the enhancement of agro-biodiversity. The world\u0026rsquo;s mango production is dominated by India, which accounts for 40\u0026ndash;45% of the world\u0026rsquo;s mango output with an annual capacity above 22\u0026nbsp;million tonnes, from approximately 2.6\u0026nbsp;million hectares (Balaganesh et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; NHB, 2024). In turn, commercial intensive cultivation has caused escalated input costs, decreasing nutrient utilization efficiency, erosion of the soil, and diminished profit potential, most especially in high-input systems (Adak et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Umar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These challenges highlighted the importance of adaptive nutrient patterns to modern orchard systems. Dashehari, an early-maturing cultivar known for its flavor and market demand, accounts for the overwhelming majority of cultivation in the subtropical Tarai region of northern India (Kuldeep et al., 2025; Singh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Conventional (low density) planting (10 \u0026times; 10 m; ~100 trees ha⁻\u0026sup1;) has been replaced over time with medium density (5 \u0026times; 5 m; 400 trees ha⁻\u0026sup1;), leading to 20\u0026ndash;50% improvement in land utilization and per hectare productivity (Oosthuyse, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dutta et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ram et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such high-capacity systems can generate an early stage of bearing and enhance canopy management, but the need for careful nutrition to prevent shading, nutrient depletion, and alternate bearing (Menzel, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gaikwad et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). 5 \u0026times; 5 m spacing is proven in Dashehari orchards to lead to a high increase in yield characteristics with full fertilization as well, yet maximum recommended fertilizer (RDF\u0026thinsp;=\u0026thinsp;1000 g N\u0026thinsp;+\u0026thinsp;750 g P₂O₅ + 1000 g K₂O\u0026thinsp;+\u0026thinsp;50 kg FYM tree⁻\u0026sup1; year⁻\u0026sup1;) is charged in addition of economic costs and environmental detriment (Biswas, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Malihabad region trials, 2025). Integrated Nutrient Management (INM) that uses reduced RDF in combination with manures, plant organic manure, and soil- or foliar-targeted micronutrients to optimize nutrient availability, revive soil microbial activity, and retain fruit quality without loss of yield (Srivastava, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vikalp et al., 2022; Harsh et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In perennial fruit crops like mango, INM is associated with improved root proliferation, dehydrogenase, urease, phosphatase enzyme, arbuscular mycorrhizal fungi (AMF) spore density, and leaf chlorophyll content, and decreased nutrient loss by fixation and leaching (Mishra et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kheir et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Two-year experiment carried out to obtain this effect in two-year field studies, Dashehari mango in medium-density-planting environment, has been performed, revealing that between 75%-50% RDF supplemented with foliar micronutrient sprays (Fe, Zn, B, Ca) enhances post-harvest soil quality status (Kuldeep et al., 2025; Umar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; El-Salhy et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), leading to 15\u0026ndash;30% higher fruit yield. The use of INM also enhances favorable plant-microbe feedback and climate-resilient production practices in subtropical orchards (Srivastava \u0026amp; Singh, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Harsh et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Financial justification still represents the determining factor in the uptake of any nutrient management practice by small and marginal mango producers (Adnan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ndiwa et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Benefit-cost analysis can offer an alternative quantitative metric for determining the profitability of food production by comparing overall gross income from yield to total cultivation cost and plant fertilizers, micronutrients used with their work, labour, and fixed operations (Gupta et al., 2022; Pant et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The B:C ratios achieved in mango-growing fruit studies published recently show B:C values 1.84 under 100% RDF control to 2.96 under 75% RDF\u0026thinsp;+\u0026thinsp;two foliar micronutrient sprays (without soil application). With lower fertilizer prices, higher marketable fruit yields (up to 22.68 t ha⁻\u0026sup1;), and premium fruit quality, which can fetch better prices (Kuldeep et al., 2025; Devi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Singh and Banik, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In soil-applied micronutrient treatments, the B:C ratio tends to decline, often resulting from increased input costs but no corresponding increase in yield (Bana et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dass et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Net income and B:C ratio is highly positively correlated with yield, indicating that careful RDF reduction accompanied by focused foliar nutrition will yield maximum profitability while reducing dependence on chemical fertilizer (Kumar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gourkhede et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Pearson correlation and Partial Least Squares Structural Equation Modelling (PLS-SEM) are reliable multivariate methods to unify complex interrelationships between nutrient management practices, soil-plant nutrient pockets, yield, and economic parameters (Tama et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Şeng\u0026uuml;l et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Pearson correlation is used to identify linear relationships (B ratio and net income, r\u0026thinsp;=\u0026thinsp;0.868, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and PLS-SEM is used to simultaneously estimate direct, indirect, moderating, and effect in two or more latent variables (Hair et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bunkus et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such methods have been very successfully used in agricultural economics for modeling farmer adoption behavior, risk perception, sustainability attributes, and nutrient-use productivity in perennial and conservation agriculture systems (Aziz et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bouyghrissi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Unlike standard ANOVA, PLS-SEM provides causal pathways (eg, B:C ratio \u0026rarr; net income) and moderator variables, and ultimately generates evidence-informed policy suggestions for resource-limited tropical agroecosystems (Paramesh et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notwithstanding much agronomic expertise on INM in mango, multivariate economic modelling of B:C ratio, yield, and net income responses to medium-density planting is still rare (Srivastava, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lingwan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jayasinghege et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The majority of the presented studies use non-experimental analysis of yield or soil health measures and miss causal pathways between the reduced RDF\u0026thinsp;+\u0026thinsp;foliar micronutrients and the economic impact of PLS-SEM (Ravikiran, 2018; Ahmad et al., 2018; Shagun et al., 2024). Cultivar-specific (Dashehari) and location-specific (Tarai region) data for 75% RDF\u0026thinsp;+\u0026thinsp;two foliar sprays without soil micronutrients are especially limited, especially for progressive fertilizer reduction regimes (100\u0026thinsp;\u0026minus;\u0026thinsp;25% RDF) (Oosthuyse, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Singh et al., 2017). This study aims to fill these gaps by studying the direct, indirect, and moderating effects of ten INM treatments on economic indicators using Pearson correlation and PLS-SEM analysis in a 13-year-old medium-density Dashehari mango orchard. The results are expected to offer sustainable, economical, and evidence-based nutrient management guidelines for tropical perennial agroecosystems to promote acceptance of various INM approaches among mango farmers.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOrchard site and experimental period\u003c/h2\u003e \u003cp\u003eThe study was conducted during 2023\u0026ndash;2024 at the Horticulture Research Centre, Patharchatta, GBPUA\u0026amp;T, Pantnagar, Uttarakhand (\u0026asymp;\u0026thinsp;29.5\u0026deg;N, 79.3\u0026deg;E; 244 m a.s.l.; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The experiment used a medium-density mango (\u003cem\u003eMangifera indica\u003c/em\u003e L. cv. Dashehari) orchard of uniform 13-year-old trees planted at 5 \u0026times; 5 m spacing (400 trees ha⁻\u0026sup1;).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoil and climate\u003c/h3\u003e\n\u003cp\u003eThe site lies in the Tarai agro-ecological zone with a sub-humid subtropical climate. The experimental soil is silty clay loam (Patharchatta Series II, Mollisol).\u003c/p\u003e\n\u003ch3\u003eExperimental design and treatments\u003c/h3\u003e\n\u003cp\u003eThe experiment was laid out in a randomized block design (FRBD) with 10 treatments and three replications. Treatments evaluated combinations of full, reduced (75%, 50%, 25%) recommended fertilizer dose (RDF\u0026thinsp;=\u0026thinsp;1000 g N\u0026thinsp;+\u0026thinsp;750 g P₂O₅ + 1000 g K₂O\u0026thinsp;+\u0026thinsp;50 kg FYM tree⁻\u0026sup1; year⁻\u0026sup1;) and soil-applied micronutrients (Fe, Cu, Zn each 100 g tree⁻\u0026sup1; and B 50 g tree⁻\u0026sup1;), together with one or two foliar sprays of micronutrients. In brief treatment details:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e (Control): 100% RDF applied in the basin after harvest.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e-T\u003csub\u003e4\u003c/sub\u003e: 75%, 50%, 25% RDF, respectively, + soil micronutrients (Fe, Cu, Zn @100 g each +\u0026thinsp;B @50 g tree⁻\u0026sup1;)\u0026thinsp;+\u0026thinsp;one foliar spray (Fe, Ca, Zn each 0.50% + B- 0.10%) (just before flowering and marble stages as a single event).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eT\u003csub\u003e5\u003c/sub\u003e-T\u003csub\u003e7\u003c/sub\u003e: 75%, 50%, 25% RDF, respectively, + soil micronutrients (as above)\u0026thinsp;+\u0026thinsp;two foliar sprays (same foliar composition; applied just before flowering and at marble stage).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eT\u003csub\u003e8\u003c/sub\u003e-T\u003csub\u003e10\u003c/sub\u003e: 75%, 50%, 25% RDF, respectively, without soil micronutrients but with two foliar sprays (just before flowering and at marble stage).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eOrchard management and fertilizer application\u003c/h3\u003e\n\u003cp\u003eBasins (radius: 1.5 m from trunk) were prepared around each tree. Farmyard manure was applied in October. P, K, and micronutrient basal doses were incorporated into the basins in November. Nitrogen was applied in two equal splits: first immediately after flowering and the second at the mustard-to-pea stage of fruit development. Foliar sprays (10 L water tree⁻\u0026sup1; per spray) were prepared by dissolving micronutrient salts and applied with a tractor sprayer at the timings (just before flowering and at marble stage). Standard cultural practices (irrigation, weeding, pruning, pest and disease control) were followed uniformly across treatments.\u003c/p\u003e\n\u003ch3\u003eEconomic Analysis\u003c/h3\u003e\n\u003cp\u003eFertilizer cost and fixed operational costs incurred per hectare in the mango orchard during the two-year experimental period, as presented in \u003cb\u003eSupplementary Tables\u0026nbsp;1 \u0026amp; 2a.\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGross income\u003c/h2\u003e \u003cp\u003eGross income was calculated as the total revenue obtained from the sale of mango fruits produced per hectare under each treatment \u003cb\u003eSupplementary Table\u0026nbsp;2b.\u003c/b\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{G}\\text{r}\\text{o}\\text{s}\\text{s}\\:\\text{i}\\text{n}\\text{c}\\text{o}\\text{m}\\text{e}=\\text{Y}\\text{i}\\text{e}\\text{l}\\text{d}\\:\\left(\\text{R}\\text{s}./\\text{h}\\text{a}\\right)\\times\\:\\text{M}\\text{a}\\text{r}\\text{k}\\text{e}\\text{t}\\:\\text{R}\\text{a}\\text{t}\\text{e}\\:(\\text{R}\\text{s}./\\text{k}\\text{g})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTotal cost of cultivation\u003c/h3\u003e\n\u003cp\u003eThe total cost of cultivation included the sum of all costs incurred for mango cultivation under each treatment, such as RDF cost, micronutrient cost, and fixed costs.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{c}\\text{o}\\text{s}\\text{t}\\:\\text{o}\\text{f}\\:\\text{c}\\text{u}\\text{l}\\text{t}\\text{i}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\left(\\text{R}\\text{s}./\\text{h}\\text{a}\\right)=\\text{R}\\text{D}\\text{F}\\:\\text{c}\\text{o}\\text{s}\\text{t}+\\text{M}\\text{i}\\text{c}\\text{r}\\text{o}\\text{n}\\text{u}\\text{t}\\text{r}\\text{i}\\text{e}\\text{n}\\text{t}\\:\\text{c}\\text{o}\\text{s}\\text{t}+\\text{F}\\text{i}\\text{x}\\text{e}\\text{d}\\:\\text{c}\\text{o}\\text{s}\\text{t}\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eNet income\u003c/h3\u003e\n\u003cp\u003eNet income represented the profit obtained per hectare after deducting the total cost of cultivation from gross income.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{N}\\text{e}\\text{t}\\:\\text{i}\\text{n}\\text{c}\\text{o}\\text{m}\\text{e}\\:\\left(\\text{R}\\text{s}./\\text{h}\\text{a}\\right)=\\text{G}\\text{r}\\text{o}\\text{s}\\text{s}\\:\\text{i}\\text{n}\\text{c}\\text{o}\\text{m}\\text{e}-\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{c}\\text{o}\\text{s}\\text{t}\\:\\text{o}\\text{f}\\:\\text{c}\\text{u}\\text{l}\\text{t}\\text{i}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBenefit-Cost Ratio\u003c/h2\u003e \u003cp\u003eThe benefit-cost ratio was determined to assess the economic efficiency of each treatment, showing the returns per rupee invested.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\text{B}\\text{e}\\text{n}\\text{e}\\text{f}\\text{i}\\text{t}\\:\\text{c}\\text{o}\\text{s}\\text{t}\\:\\text{r}\\text{a}\\text{t}\\text{i}\\text{o}=\\frac{(\\text{G}\\text{r}\\text{o}\\text{s}\\text{s}\\:\\text{i}\\text{n}\\text{c}\\text{o}\\text{m}\\text{e}-\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{c}\\text{o}\\text{s}\\text{t}\\:\\text{o}\\text{f}\\:\\text{c}\\text{u}\\text{l}\\text{t}\\text{i}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n})}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{c}\\text{o}\\text{s}\\text{t}\\:\\text{o}\\text{f}\\:\\text{c}\\text{u}\\text{l}\\text{t}\\text{i}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eYield (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/h2\u003e \u003cp\u003eThe yield from each tree was multiplied by the total number of trees planted per hectare, and the result was expressed in t/ha.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe experimental findings were analyzed using two-way ANOVA to determine the significant effects of recommended dose of fertilizers (RDF) levels and micronutrient application methods, as well as their interaction, on the benefit-cost (B:C) ratio, fruit yield, and net income. All parameters of interest were analyzed (N\u0026thinsp;=\u0026thinsp;30) using descriptive statistics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation). Pearson\u0026rsquo;s correlation test was conducted to investigate the relationships between fruit yield, net income, B:C ratio, and RDF level with IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA). Partial least squares structural equation modeling (PLS-SEM) was performed in order to demonstrate the direct, indirect, and moderating effects of B:C ratio on RDF level, yield, and net income with group culture (GC) and quality of experience (QE) as potential moderators. The model was estimated based on the path-weighting scheme on standardized data (maximum 3000 iterations, stopping criterion 10⁻⁷). Path coefficients, direct effects, indirect effects, and moderation effects were tested by bootstrapping with 5000 resamples using the percentile method. All statistical tests were considered significant at *p* \u0026le; 0.05 in SmartPLS (version 4.1.1.5).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCost of cultivation\u003c/h2\u003e \u003cp\u003eThe costs in operation of the mango orchard were classified as variable fertilizer costs (macronutrients NPK and FYM, and micronutrients) and fixed operation costs. All costs are per hectare over the two-year experimental period. Costs for macronutrients (urea, single superphosphate [SSP], muriate of potash [MOP], and farmyard manure [FYM]) were based on the recommended amounts of fertilizers (RDF), and scaled proportionally among treatments. Micronutrient costs appeared to differ according to the quantity, source, and method of application (soil applied plus the foliar sprays), reflecting the nature of each group treated. \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e shows the overall fertilizer cost of the ten treatments (T\u003csub\u003e1\u003c/sub\u003e-T\u003csub\u003e10\u003c/sub\u003e). Treatment T\u003csub\u003e1\u003c/sub\u003e (100% RDF) produced macronutrient total costs. Treatments T\u003csub\u003e2\u003c/sub\u003e-T\u003csub\u003e4\u003c/sub\u003e received 75%, 50%, and 25% RDF, respectively, with standard micronutrient packages. Treatments T\u003csub\u003e5\u003c/sub\u003e-T\u003csub\u003e7\u003c/sub\u003e were given the same RDF dosage, but the micronutrient contents were increased (elevated rates of Fe, Zn, B, and Ca). Treatments T\u003csub\u003e8\u003c/sub\u003e-T\u003csub\u003e10\u003c/sub\u003e received similar levels for the RDF, with a reduced micronutrient package (Fe, Zn, B, and Ca only; Cu omitted and lower overall quantities). Fixed operational costs and Gross income were comparable for all treatments \u003cb\u003e(Supplementary Table\u0026nbsp;2a \u0026amp; 2b).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBenefit-Cost (B:C) Ratio, Yield, and Net Income\u003c/h2\u003e \u003cp\u003eThe Benefit-Cost (B:C) ratio was also influenced strongly by the appropriate fertilizer doses recommended (RDF) and the type of micronutrient applied (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e In all treatments, the B:C ratios ranged from 0.32 to 2.96, over 1.0 for profitable returns. B:C increased most under 25% RDF if the micronutrient application in two foliar sprays only (1.65), and soil micronutrient application in combination with a single or two foliar sprays was markedly lower (0.45 and 0.32, respectively). The same trend was also noted in 50% RDF, with two foliar sprays alone yielding the highest B:C (2.77) ratio, compared to 0.79 and 0.72 for soil and foliar treatments. At 75% RDF, a maximum B:C ratio at 2.96 was obtained through two foliar sprays of micronutrients alone, which was significantly better compared with both soil-plus-single-foliar (0.90) and soil plus two-foliar (1.13) combinations. The B:C ratio of 100% RDF control (no micronutrients) was 1.84. When averaged across the 25%, 50% and 75% RDF levels, the main effect of micronutrient application method showed that only two foliar sprays produced the highest mean B:C ratio (2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71), which was more than three times greater than the ratios with soil micronutrients plus single foliar spray (0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23) or soil micronutrients plus two foliar sprays (0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41). The 100% RDF control without micronutrients had a B:C of 1.84. Overall, the highest B:C ratio recorded in the entire experiment was 2.96 under the treatment combination of 75% RDF\u0026thinsp;+\u0026thinsp;two foliar sprays of micronutrients. The highest fruit yield (22.68 t ha⁻\u0026sup1;) and net income (508.88 \u0026times; 10\u0026sup3; Rs. ha⁻\u0026sup1;) were obtained with this treatment compared to the combination used in our study. Whereas, the soil-containing micronutrients consistently resulted in the B:C ratio decline below 100% at every RDF level, and the difference was due mainly to higher cost, but less fruit yield, and marginal return. Therefore, strategic reduction of RDF to 75% alongside two foliar sprays of micronutrients (without soil application) maximized the economic benefit of the method by yielding a superior B:C ratio over both full-dose fertilization and any soil-plus-foliar regime.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of RDF levels and micronutrient application methods on yield, net income, and benefit-cost (B:C) ratio, mean of two years of data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDF Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicronutrients Regime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYield\u003c/p\u003e \u003cp\u003e(t ha-\u003csup\u003e1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNet Income (10\u003csup\u003e3\u003c/sup\u003e Rs. /ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB: C Ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Micronutrients\u0026thinsp;+\u0026thinsp;Single Foliar Spray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Micronutrients\u0026thinsp;+\u0026thinsp;Two Foliar Sprays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo Foliar Sprays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.86\u0026thinsp;\u0026plusmn;\u0026thinsp;67.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Micronutrients\u0026thinsp;+\u0026thinsp;Single Foliar Spray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Micronutrients\u0026thinsp;+\u0026thinsp;Two Foliar Sprays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo Foliar Sprays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e411.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e270.93\u0026thinsp;\u0026plusmn;\u0026thinsp;121.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Micronutrients\u0026thinsp;+\u0026thinsp;Single Foliar Spray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Micronutrients\u0026thinsp;+\u0026thinsp;Two Foliar Sprays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo Foliar Sprays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e508.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366.34\u0026thinsp;\u0026plusmn;\u0026thinsp;133.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl (No Micronutrients)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe Main effect of the micronutrient\u0026rsquo;s application method\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e100% RDF (Without soil and foliar spray of micronutrients)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e25, 50 and 75% RDF\u0026thinsp;+\u0026thinsp;Soil micronutrients\u0026thinsp;+\u0026thinsp;Single foliar spray of micronutrients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179.80\u0026thinsp;\u0026plusmn;\u0026thinsp;72.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e25, 50 and 75% RDF\u0026thinsp;+\u0026thinsp;Soil micronutrients\u0026thinsp;+\u0026thinsp;two foliar sprays of micronutrients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211.71\u0026thinsp;\u0026plusmn;\u0026thinsp;132.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e25, 50 and 75% RDF\u0026thinsp;+\u0026thinsp;two foliar sprays of micronutrients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e375.62\u0026thinsp;\u0026plusmn;\u0026thinsp;154.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252.67\u0026thinsp;\u0026plusmn;\u0026thinsp;133.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics and Pearson Correlation Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics of study variables (N\u0026thinsp;=\u0026thinsp;30) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Mean (\u0026plusmn;\u0026thinsp;standard deviation) values: Yield\u0026thinsp;=\u0026thinsp;18.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20, Net Income\u0026thinsp;=\u0026thinsp;252.62\u0026thinsp;\u0026plusmn;\u0026thinsp;129.30, B:C ratio\u0026thinsp;=\u0026thinsp;1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90, and RDF level\u0026thinsp;=\u0026thinsp;55.00\u0026thinsp;\u0026plusmn;\u0026thinsp;24.91. Pearson correlation analysis \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e showed a statistically significant positive relationship between the B:C ratio and RDF level (r\u0026thinsp;=\u0026thinsp;0.410, P\u0026thinsp;=\u0026thinsp;0.025). The result demonstrates that higher RDF levels are associated with higher C:B (Cost-Benefit) ratios for this dataset. Nevertheless, lowering the RDF is assumed to lower the expenditure on the cultivation phase considerably, mainly through reduced agricultural fertilizer inputs. Cutting cultivation costs can also increase the benefit-cost (B:C) ratio, indicating that prudently lowering RDF levels can improve the overall economic efficiency, as long as yield decreases are minimal. The B:C ratio also reported strong, positive correlations with Net Income (r\u0026thinsp;=\u0026thinsp;0.868, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and moderate positive correlations with Yield (r\u0026thinsp;=\u0026thinsp;0.395, P\u0026thinsp;=\u0026thinsp;0.031). All these correlations were significant at the level of significance of 0.01 or 0.05 (two-tailed test).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics (Mean, Std. Deviation, N) for all four variables (Yield, Net Income, B:C Ratio and RDF Level).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYield (t/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNat Income (Rs.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB:C Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRDF Level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eYield (t/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNet Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eB:C Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRDF Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYield (t/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.85\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB:c Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDF level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePartial Least Squares Structural Equation Modelling (PLS-SEM: Path Coefficient)\u003c/h2\u003e \u003cp\u003eBenefits-to-cost (B:C) Ratio has a direct impact on RDF Level, Yield, Net Income, which were estimated using partial least squares structural equation modelling (PLS-SEM) method, and group culture (GC) and QE were proposed as potential moderators. Model estimation was implemented based on the method of path-weighting with the standardized data (the maximum 3000 iterations, stopping point 10⁻⁷; full settings in \u003cb\u003eSupplementary Tables S3-S4\u003c/b\u003e). The structural model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) has accounted for high variability among endogenous constructs (R\u0026sup2; = 0.421 for Yield, R\u0026sup2; = 0.548 for RDF Level and R\u0026sup2; = 0.989 for Net Income). In Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, direct standardized path coefficients, bootstrap means, standard deviations, t-statistics, and p-values (5000 resamples, percentile method) are described. The B:C Ratio was our main predictor, having two statistically significant direct associations. It had a significant direct positive influence on Net Income (β\u0026thinsp;=\u0026thinsp;0.560, t\u0026thinsp;=\u0026thinsp;3.154, p\u0026thinsp;=\u0026thinsp;0.002), and a significant negative direct impact on Yield with (β = \u0026minus;2.309, t\u0026thinsp;=\u0026thinsp;2.147, p\u0026thinsp;=\u0026thinsp;0.032). The inverse path from B:C Ratio to RDF Level was not significant, although (β\u0026thinsp;=\u0026thinsp;0.266, t\u0026thinsp;=\u0026thinsp;0.211, p\u0026thinsp;=\u0026thinsp;0.833). Moderation of significant effect by the B:C Ratio was also observed as well. GC (B:C Ratio \u0026rarr; Yield) was a positive effect on Yield (β\u0026thinsp;=\u0026thinsp;2.644, t\u0026thinsp;=\u0026thinsp;2.703, p\u0026thinsp;=\u0026thinsp;0.007), and QE (B:C Ratio) a positive moderating effect on Yield (β\u0026thinsp;=\u0026thinsp;0.590, t\u0026thinsp;=\u0026thinsp;2.454, p\u0026thinsp;=\u0026thinsp;0.014). All the additional paths were non-significant - RDF Level as an end-point or mediator and other direct paths included were non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; detailed statistics in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Indirect effects total and specific to the B:C Ratio were also non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Indirect effect of B:C Ratio on Net Income was \u0026minus;\u0026thinsp;0.742 (95% CI\u0026thinsp;\u0026minus;\u0026thinsp;5.425 to 2.804, p\u0026thinsp;=\u0026thinsp;0.731), and no specific indirect pathway, mediated by RDF Level or Yield, reaches statistical significance \u003cb\u003e(Supplementary Tables S5-S8).\u003c/b\u003e Bootstrap distributions (\u003cb\u003eSupplementary Figs. S1-S3)\u003c/b\u003e established the robustness of such estimates by finding approximately normal distributions which were found to be centered in the vicinity of the sample mean and were relatively non-bias biased. These results show that the B:C Ratio positively drives Net Income, negatively drives Yield with GC and QE representing major moderation of the relationship between B:C Ratio \u0026rarr; Yield.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect path coefficients (standardized) - Mean, STDEV, T-values, p-values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal sample (O)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample mean (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard deviation (STDEV)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB:C Ratio \u0026rarr; Net Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB:C Ratio \u0026rarr; RDF Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB:C Ratio \u0026rarr; Yield\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGC (B:C Ratio \u0026rarr; Net Income)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGC (B:C Ratio \u0026rarr; RDF Level)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGC (B:C Ratio \u0026rarr; Yield) \u0026rarr; Yield\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGC (RDF Level \u0026rarr; Net Income)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGC (Yield \u0026rarr; Net Income)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGC (Yield \u0026rarr; RDF Level) \u0026rarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQE (B:C Ratio) \u0026rarr; RDF Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQE (B:C Ratio) \u0026rarr; Yield\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQE (RDF Level) \u0026rarr; Net Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQE (Yield) \u0026rarr; Net Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRDF Level \u0026rarr; Net Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYield \u0026rarr; Net Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYield \u0026rarr; RDF Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEconomic Efficiency of Reduced RDF Combined with Foliar Micronutrients\u003c/h2\u003e \u003cp\u003eThe B:C (benefit-cost) ratio and micronutrient doses depend on RDF levels and the application process. The maximum B:C ratio (2.96) was obtained with 75% RDF (two foliar sprays of micronutrients \u0026amp; no soil application of micronutrients). This was also the treatment that gave the highest yield of fruit (22.68 t ha⁻\u0026sup1;) and generated the highest net incomes (508.88 \u0026times; 10\u0026sup3; Rs. ha⁻\u0026sup1;). Simultaneously, soil micronutrient content-based and foliar sprays also induced B:C ratios (0.32\u0026ndash;1.13) that were significantly lower at all lower RDF concentrations (25\u0026ndash;75%), resulting in higher input costs, no yield gain, and no return. A moderate B:C ratio of 1.84 was achieved for the 100% RDF control (no micronutrients). This establishes the economic merits of the reduction of RDF to 75% supplemented with foliar micronutrient application. In terms of efficiency and productivity, foliar sprays allow for rapid leaf processing without soil fixation, leaching, and immobilization loss. Planting the soil provided additional micronutrient input, increasing its cost and reducing agricultural utility, where the average B:C ratio for the two foliar sprays, only (2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71) in the treated low quantity of RDF, was significantly greater than the soil plus foliar results. This is in line with the findings of Bana et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), 75\u0026ndash;100% RDF foliar micronutrient-embedded NPK resulted in substantially better eggplant yield, biofortification, and economy. Similarly, Dass et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that foliar macro- and micronutrients of soybean at lower RDF levels result in an increased soybean seed yield (18.5\u0026ndash;37.8%), net returns (21\u0026ndash;58%), and B:C ratio. Ahmed et al. (2024) and other studies concluded that foliar application results in a better recovery of nutrients and economic returns than soils, especially considering low RDF, since fixation leads to horticulture soil pH. Niu et al. (2020) found chelated foliar micronutrients reduced overall fertilizer input by 15\u0026ndash;30% at targeted delivery stages during growth and increased yield and B:C ratio. These findings are supported by recent mango-specific studies: 75% RDF in combination with two foliar micronutrient sprays had the highest B:C ratio, higher fruit yield, and net returns in mango cv. Dashehari under medium density planting (Kuldeep et al., 2025) and foliar micronutrient applications (in the absence or presence of reduced RDF) considerably increased the yield, quality, and economic efficiency of other mangoes over soil-only methods (Umar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Overall, this information supports the principle of integrated nutrient management (INM), implying that for sustainable horticultural production, 75% RDF and two foliar micronutrient sprays - without soil - produce optimized economic returns and decrease the amount of chemical fertilizer required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation among Yield, Net Income, and B:C Ratio\u003c/h2\u003e \u003cp\u003ePearson correlation analysis indicated significant positive relationships between fruit yield, net income, B:C ratio, and the recommended dose of fertilizers (RDF) level. It was found that the fruit yield was in positive relations and relatively consistent with the net income (r\u0026thinsp;=\u0026thinsp;0.77, P\u0026thinsp;=\u0026thinsp;0.001) and moderate with the RDF level (r\u0026thinsp;=\u0026thinsp;0.57, P\u0026thinsp;=\u0026thinsp;0.001) and the B:C ratio (r\u0026thinsp;=\u0026thinsp;0.39, P\u0026thinsp;=\u0026thinsp;0.031). Net income was very strongly associated with B:C ratio (r\u0026thinsp;=\u0026thinsp;0.87, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the RDF level (r\u0026thinsp;=\u0026thinsp;0.53, P\u0026thinsp;=\u0026thinsp;0.003) was also moderately associated. The B:C ratio did moderately show a positive relationship with RDF (r\u0026thinsp;=\u0026thinsp;0.41, P\u0026thinsp;=\u0026thinsp;0.025). These associations suggest that both productivity and economic gain were associated with optimum RDF levels in general. The highest B:C ratio (2.96) and maximum fruit yield (22.68 t ha⁻\u0026sup1;), however, were obtained at 75% RDF supplemented by two foliar micronutrient sprays (without soil application). This highlights the capacity of strategic reductions of RDF with high-precision foliar delivery to more effectively achieve economic returns than application when taken in full doses in achieving optimal nutrient-use efficiency with lower input costs. These relationships correspond to integrated nutrient management (INM) principles and are in line with previous studies. Bana et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Dass et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported significant positive correlations between yield, net returns, and B:C ratio under foliar micronutrient supplementation at reduced RDF levels in eggplant and soybean, respectively. This is consistent with other work by Rathore et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in tomato. In mango, the foliar micronutrient applications (with or without reduced RDF) have also increased both yield and quality parameters and economic returns by means of enhanced nutrient recovery (Umar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recent research specific to mango has corroborated this finding showing that 75% RDF\u0026thinsp;+\u0026thinsp;two foliar micronutrient sprays can optimize associations among yield, net income, and B:C ratio while maximizing economic efficiency under medium-density planting (Kuldeep et al., 2025).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePLS-SEM Path Coefficient and Moderation\u003c/h2\u003e \u003cp\u003eBy means of partial least squares structural equation modelling (PLS-SEM), we showed significant significance for causative relationships among benefit-cost (B:C) ratio, fruit yield, net income and recommended dose of fertilizers (RDF) level. The model had high explanatory power and explained 42.1% variability in fruit yield, 54.8% variance in RDF level, and 98.9% variance in net income. The B:C ratio exerted significant direct positive effect on net income (β\u0026thinsp;=\u0026thinsp;0.560, t\u0026thinsp;=\u0026thinsp;3.154, p\u0026thinsp;=\u0026thinsp;0.002), indicating that economic efficiency is a main basis of profitability in integrated nutrient management (INM) systems (Dass et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Paramesh et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, the B:C ratio had a direct, negative impact on fruit yield (β = \u0026minus;2.309, t\u0026thinsp;=\u0026thinsp;2.147, p\u0026thinsp;=\u0026thinsp;0.032). This inverse correlation indicates the cost-savings obtained in the study, where the highest B:C ratio (2.96) as well as the highest fruit yield (22.68 t ha⁻\u0026sup1;) were achieved in concomitant 75% RDF and two foliar micronutrient sprays with no soil application. This treatment provided better net income with significantly lower input prices than full RDF systems in the present study (Bana et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The linear relationship from B:C ratio to RDF level was not significant (β\u0026thinsp;=\u0026thinsp;0.266, p\u0026thinsp;=\u0026thinsp;0.833), and indirect impact too were non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The combination of these PLS-SEM findings supports the previous analysis using ANOVA and Pearson correlation by presenting positive effects of the B:C ratio on net income and revealing important trade-off effects of fine-tuned foliar nutrition. The results corroborate with previous INM study where low RDF content in conjunction with foliar micronutrient application enhances nutrient use efficiency and sustains high productivity, leading to positive economic returns in general without yield penalties (Kumar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gourkhede et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The results of a study of mango by the same PLS-SEM technique with respect to cv. Dashehari at medium density planting showed that plant nutrient status (which significantly correlated with 75% RDF\u0026thinsp;+\u0026thinsp;foliar sprays) was the most important driver of fruit yield, where the same treatment would result in the highest B:C ratio and economic performance (Kuldeep et al., 2025). Combined, these findings indicate that 75% RDF\u0026thinsp;+\u0026thinsp;two foliar micronutrient sprays (without soil application) represent the most economical and environmentally friendly choice of mango production.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that integrated nutrient management (INM) implemented for medium-density Dashehari mango orchards can be greatly enhanced by prudently lowering the recommended amount of fertilizer (RDF) and supplementing it exclusively with foliar micronutrient sprays without soil application. These treatment types produced the greatest, highest B:C ratio in terms of total fruit yield and maximum net income in contrast to the full RDF control and every treatment with a soil-applied micronutrient. Soil-plus-foliar regimes always reduced economic returns, resulting in increased input costs without increases in yield or productivity. Pearson correlation analysis demonstrated a significant correlation between B:C ratio, net income, and fruit yield. Partial least squares structural equation modelling (PLS-SEM) showed that: B:C ratio significantly positively affects net income; group culture and quality of experience positively moderate B:C-yield link. These results demonstrate that accurate delivery of foliar micronutrients with lower RDF improves the nutrient utilization efficiency and profits while avoiding reliance on chemical fertilizer treatment for maintaining high productivity. The method describes the development of an alternative, sustainable, and economically viable approach to Dashehari mango cultivation, in the context of medium-density planting systems in subtropical habitats. This INM protocol should be validated in other cultivars, in other soils, and during extended growth cycles in future studies, which in turn can support more extensive utilization in tropical horticulture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcknowledgment is extended for the financial assistance and infrastructural support provided by the Horticulture Research Centre, Patharchatta, and the Department of Horticulture, GBPUA\u0026amp;T, Pantnagar, Uttarakhand, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions made in the study are contained in the article/supplementary materials. \u0026nbsp; Additional question can be referred to the relevant authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, investigation and original draft writing done by Kuldeep, \u0026amp; AKS. Methodology and statistical analysis were done by AB, OS. Review, editing and supervision were done by SPP, SCS, HCJ, MY \u0026amp; MKB.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved field experimentation on mango (Mangifera Indica L.) plants and did not involve human participants or animals. Therefore, formal ethical approval was not required. All experimental procedures were conducted following standard agronomic research practices and the institutional guidelines of Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of AI-assisted writing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors exclusively used OpenAI's ChatGPT (GPT-5 model) to improve the clarity, grammar, and style of the manuscript. The authors carefully reviewed, edited, and approved all AI-generated suggestions and take full responsibility for the final content of the paper.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdak T, Kumar A, Singh R, Singh V (2022) Response of Dashehari mango to different zinc levels. 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J Agricultural Res Technol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.56228/JART.2022.SP108\u003c/span\u003e\u003cspan address=\"10.56228/JART.2022.SP108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResult, Table\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Govind Ballabh Pant University of Agriculture and Technology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Benefit Cost Ratio (B:C Ratio), Net Income, Mango, RDF, PLS-SEM, Correlation","lastPublishedDoi":"10.21203/rs.3.rs-9456371/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9456371/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eThis study aimed to evaluate the effects of integrated nutrient management (INM) strategies involving reduced recommended fertilizer doses (RDF) and different micronutrient application methods on benefit-cost (B:C) ratio, fruit yield, and net income in medium-density Dashehari mango.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe two-year (2023\u0026ndash;2024) experiment was conducted in a 13-year-old Dashehari mango orchard at Pantnagar, Uttarakhand, using a factorial randomized block design with ten treatments and three replications. Treatments included 100, 75, 50, and 25% RDF combined with soil and/or foliar applications of micronutrients. Economic parameters (cost of cultivation, gross income, net income, B:C ratio) and yield were recorded and analyzed using two-way ANOVA, Pearson correlation, and PLS-SEM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe combination of 75% RDF plus two foliar micronutrient sprays (no soil application) recorded the highest B:C ratio of 2.96, maximum fruit yield (22.68 t ha⁻\u0026sup1;), and highest net income (508.88 \u0026times; 10\u0026sup3; Rs. ha⁻\u0026sup1;). This treatment outperformed the 100% RDF control (B:C 1.84). Strong positive correlation was observed between B:C ratio and net income (r\u0026thinsp;=\u0026thinsp;0.868, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). PLS-SEM showed B:C ratio had a significant positive effect on net income (β\u0026thinsp;=\u0026thinsp;0.560, p\u0026thinsp;=\u0026thinsp;0.002) and explained 98.9% of its variance. Group culture and quality of experience positively moderated the B:C ratio to yield a relationship.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eReduction of RDF to 75% combined with two foliar micronutrient sprays without soil application proved most profitable and sustainable for Dashehari mango cultivation under medium density planting by enhancing economic returns and nutrient use efficiency while reducing fertilizer dependency.\u003c/p\u003e","manuscriptTitle":"Multivariate Economic Modeling (PLS-SEM) and Pearson Correlation of B:C Ratio, Yield, and Net Income Responses to INM Treatments in Dashehari Mango under Medium Density Planting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 09:05:25","doi":"10.21203/rs.3.rs-9456371/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1ff0287-dd58-4efd-8aab-1d8ed3b2b927","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66575064,"name":"Horticulture"}],"tags":[],"updatedAt":"2026-04-21T09:05:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 09:05:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9456371","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9456371","identity":"rs-9456371","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}