Effects of Watering Regimes and Planting Density on Taro (Colocasia Esculenta) Growth and Yield Under Moisture Beds in Embu, Kenya

Abstract

Taro (Colocasia esculenta (L.) Schott) is one of the underutilized crops and is considered an orphan crop. In the tropics, taro is an important staple food in the human diet. Taro has suffered low production in Kenya due to various factors among them its utilization. Taro is referred to as nduma in Kenya and is primarily grown in the riverbeds. However, the riverbeds are already a limited resource due to climate change and during periods of water scarcity. There is a need to understand the edaphic and crop management factors that affect taro production to increase its production in the country. The study was conducted at KALRO – Embu research station for three growing seasons: long rains (LR) 2021, short rains (SR) 2021/2022, and long rains (LR) 2022. The main objective of the study was to determine the optimal watering regime and planting density for improved taro yields in the semi-humid areas of Kenya. Specifically, under the different watering regimes and planting densities, to determine the growth, yield, and yield components response of taro; to determine the water use efficiency (WUE) of taro; and to assess the cost and benefits of growing taro. The taro variety used in this study was Dasheen which is characterized by relatively large tubers. A factorial experiment with a split-plot layout arranged in a completely randomized block design was used. The main factor was the irrigation watering regimes while the subfactor was the planting density, with three replications. The watering regimes were 100 %, 60 %, and 30 % based on the field capacity (FC). The planting densities were 0.5m × 0.5m (40,000 plants ha-1), 1m × 0.5m (20,000 plants ha-1), and 1m × 1m (10,000 plants ha-1), representative of high, medium, and low plant densities respectively. The growth and yield component data collected in the field was recorded in data collection sheets and thereafter the data was entered into a Microsoft Excel spreadsheet and later imported to the GenStat statistical software for data analysis. The growth components data was subjected to repeated measures analysis of variance using the GenStat statistical software as the growth components data were observed repeatedly over time (days after planting) across each season. The yield components and water use efficiency data were subjected to split-plot analysis of variance using the GenStat statistical software...