AUTHOR=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 

TITLE=Deficit irrigation scheduling with mulching and yield prediction of guava (Psidium guajava L.) in a subtropical humid region

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.1044886

DOI=10.3389/fenvs.2022.1044886

ISSN=2296-665X

ABSTRACT=Drip irrigation and mulching are often used to alleviate the contradiction of poor water management in many of the crops but these technologies have not been tried yet to apply water at critical stages of guava orchard in subtropical humid Tarai regions of India for improving yield and quality. A field experiment was conducted during 2020 and 2021 which included three irrigation strategies-severe deficit irrigation (DI50), moderate deficit irrigation (DI75) and full irrigation (FI100) and four mulching methods-Silver-black mulch (MSB), black mulch (MB), organic mulch (MOM) and control without mulch (MWM). The results have shown that the relative leaf water content (RLWC) and proline content showed an increasing trend with decrease in irrigation regime resulting 123% increase in proline content under DI50 compared to FI100 while the greater plant growth was recorded with fully irrigated plants along with silver-black mulch. The leaf nutrient analysis has also shown that FI100 and MOM produced significantly higher concentration of all the nutrients. However, moderate deficit irrigation (DI75) along with silver-black mulch (MSB) produced higher numbers of fruits per plant, average fruit weight, fruit yield and maximum ascorbic acid content. The irrigation water productivity (IWP) decreased with increase in irrigation regime from severe water deficit to full irrigation resulting 33.79 per cent improvement in IWP under DI50 as compared to FI100. The 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 based on stepwise regression were leaf proline, leaf Cu, fruit weight and IWP those affected the fruit yield of guava.