Light Intensity and Nutrient Status for Accurate Yield Prediction of Mango Orchards
摘要
Among various production factors, light intensity and nutritional status of soil are crucial factors for mango productivity. The use of crucial production factors such as physiological parameters, light availability and soil nutritional status is essential for accurate and advanced yield prediction and decision support for producers. An experiment was conducted on mango orchards in and around Lucknow, Uttar Pradesh, India, during 2023 and 2024 using data on light intensity, nutritional status, yield, tree growth status, spread, canopy spread and canopy height for mathematical analysis aimed at identifying crucial factors responsible for determining yield prediction of the mango variety ‘Dashehari’. The range of light intensity was recorded as 41.16–98.8%, canopy height as 7.0–19.8 m and trunk girth as 1.15–2.69 m among the studied ‘Dashehari’ orchards. Although trees were planted at 10 × 10 m, the canopy spread was noted in the range of 7.65–21.96 m. Great variation was recorded with respect to yield (28.10–280.25 kg tree−1). Electrical conductivity (EC; 0.08–1.76 dS m−1) pH (4.59–7.5), soil organic matter content (SOC; 0.34–1.63%), as well as nitrogen and phosphorus content in orchard soil (84.83–240 kgha−1 and 3.40–12.75 kg ha−1, respectively) were recorded. Most of the mango orchard soil had a pH of not more than 7.5, as this level ensured essential nutrients were easily available for optimum dry matter production required for the yield. A positive correlation was observed between yield and light intensity (0.87), trunk girth (0.34) and organic carbon content (0.62), while a negative correlation was found with EC (−0.26) and pH value (−0.08) of soil. The variable importance plot analysis revealed that yield prediction with loading values > 0.80, i.e., light intensity, EC, N, P, K content of soil, have an impact on yield prediction. Further, the similarity analysis exhibited no significant difference among 13 factors associated with yield. Multilinear regression analysis (MLRA) showed a strong and significant collective effect between all factors. Yield attributes, light intensity, pH and EC were significant predictors in the partial least squares model for yield prediction of mango orchard variety ‘Dashehari’.