Deficit irrigation scheduling with mulching and yield prediction of guava (Psidium guajava L.) in a subtropical humid region - Jat, Rajkumar; Singh, V. P.; Ali Abed, Salwan; Al-Ansari, Nadhir; Singh, P. K.; Vishwakarma, Dinesh Kumar; Choudhary, Ashok; Al-Sadoon, Mohammad Khalid; Popat, Raj C.; Jat, Suresh Kumar (Frontiers in Environmental Science, 2022)
Drip irrigation and mulching are often used to alleviate the problem of poor water management in many crops; however, these technologies have not yet been tested for applying water at critical stages of guava orchard growth in subtropical humid Tarai regions of India to improve the yield and quality. A field experiment was conducted over 2020 and 2021 which included three irrigation strategies: severe deficit irrigation (DI50), moderate deficit irrigation (DI75), and full irrigation (FI100), as well as four mulching methods: silver-black mulch (MSB), black mulch (MB), organic mulch (MOM), and a control without mulch (MWM). The results showed that both the relative leaf water content (RLWC) and the proline content exhibited an increasing trend with a decrease in the irrigation regime, resulting in a 123% increase in the proline content under DI50 conditions compared with FI100, while greater plant growth was recorded in fully irrigated plants and using silver-black mulch. Leaf nutrient analysis showed that FI100 and MOM produced significantly higher concentrations of all nutrients. However, moderate deficit irrigation (DI75) along with silver-black mulch (MSB) produced higher numbers of fruits per plant, higher average fruit weights, higher fruit yields, and maximum ascorbic acid contents. The irrigation water productivity (IWP) decreased with an increase in the irrigation regime; from severe water deficit to full irrigation, resulting in a 33.79% improvement in IWP under DI50 conditions as compared with FI100. Regression analysis outperforms principal component regression analysis for fruit yield prediction, with adjusted R2 = 89.80%, RMSE = 1.91, MAE = 1.52, and MAPE = 3.83. The most important traits affecting the fruit yield of guava, based on stepwise regression, were leaf proline, leaf Cu, fruit weight, and IWP.