Soil quality measurement for sustainable soybean yield agroforestry system under different crop rotation models

Abstract

Abstract. Alam T, Suryanto P, Handayani S, Supriyanta, Wulandari RA, Anshari A, Aisya AW, Purba AE, Widowati R, Taryono. 2022. Soil quality measurement for sustainable soybean yield agroforestry system under different crop rotation models. Biodiversitas 23: 6155-6163. Soil quality is essential for sustaining the soybean yield in an agroforestry system. This study determined the soil quality variables that affect soybean yield under different crop rotation models in the agroforestry system with kayu putih (Melaleuca cajuputi). A 2-year experiment was conducted during the dry season (March-June 2021) and the wet season (November 2021-February 2022) at Menggoran Forest Resort, Playen District, Gunungkidul Regency, Special Province of Yogyakarta, Indonesia. All of the trials during each season were laid out in a randomized complete block design with three blocks as replication. The treatment crop rotation model consisted of soybean planting after fallow (FS), soybean planting after maize (MS), soybean planting after rice (RS), and continuous soybean planting (SS). The observations were soybean yield and 21 soil quality variables. The data were analyzed using analysis of variance, multiple factor analysis, heatmap cluster, generalized pair plot, and standardized stepwise regression. The increase in soybean yield during the dry season was affected by TB, Fe, and N in the soil, while it was affected by TF, Fe, and silt during the wet season. Soil quality and soybean yield can be improved by applying organic matter and soil amendment.

Investigating the dynamics of upland rice (Oryza sativa L.) in rainfed agroecosystems: an in-depth analysis of yield gap and strategic exploration for enhanced production

“…Moving forward, it is imperative to incorporate comprehensive variables related to climatic conditions and soil water dynamics, including soil relative humidity, water evaporation, and transpiration rates (Ansari et al, 2019). Through integrating these factors into our analyses, this research can achieve a more precise and comprehensive understanding of paddy yield dynamics in rainfed agroecosystems (Alam et al, 2022).…”