Impact of AI based Irrigation Scheduling Approaches and Drip Irrigation Methods on Yield of Chilli (Capsicum annum L.) and Chemical Properties of Soil
K. Bhavitha *
Department of Agronomy, PJTSAU, Hyderabad, Telangana, India.
Md. Latheef Pasha
Department of Agronomy, College of Agriculture, PJTSAU, Hyderabad, Telangana, India.
V. Ramulu
O/o Director of Research, Admin Block, PJTSAU, Hyderabad, Telangana, India.
T. Ram Prakash
AICRP on Weed Management, PJTSAU, Hyderabad, Telangana, India.
P. Rajaiah
AICRP on Farm Implements and Machinery, PJTSAU, Rajendranagar, Hyderabad-30, India.
P. Revathi
Water Technology Centre, PJTSAU, Hyderabad, Telangana, India.
*Author to whom correspondence should be addressed.
Abstract
Aim: To assess the effect of AI based irrigation scheduling approaches and drip irrigation methods on soil chemical properties and yield in chilli.
Study Design: The study employs drip irrigation methods as the main plots and irrigation scheduling approaches as the subplots. A split plot design was chosen as suitable design because the main plots (drip irrigation methods) need a bigger plot sizes and subplots (irrigation scheduling approaches) requires more precise results with smaller plot sizes.
Place and Duration of Study: Water Technology Centre field, College Farm, College of Agriculture, Rajendranagar, Hyderabad during rabi 2022-23 (first year) and 2023-24 (second year).
Methodology: The investigation consisted of two drip irrigation methods as main plots and four irrigation scheduling approaches as subplots with total of 8 treatment combinations replicated thrice. Data recorded on various parameters was subjected to scrutiny by ANOVA technique for split plot design concept.
Results: Green (fresh) fruit and stalk yield was found to be significantly higher under subsurface drip (41859 and 5037 kg ha-1) among drip irrigation methods; whereas, among irrigation scheduling approaches, ET sensor based irrigation triggering resulted in significantly higher green (fresh) fruit and stalk yield (43139 and 5196 kg ha-1) followed by irrigation scheduling at 1.0 Epan by manual (control) (42235 and 5065 kg ha-1). The post-harvest soil chemical properties were found to be non-significantly influenced by drip irrigation methods and irrigation scheduling approaches.
Conclusions: Subsurface drip and ET sensor based irrigation triggering resulted in higher fruit and stalk yield which might be recommended for conserving irrigation water and reducing labour use. Whereas, the drip irrigation methods and irrigation scheduling approaches did not exert any significant influence on chemical properties of post-harvest soil.
Keywords: Automation, ET sensor, fruit yield, soil chemical properties, subsurface drip