Effects of Tillage System, Sowing Date, and Weather Course on Yield of Double-Crop Soybeans Cultivated in Drained Paddy Fields

• Published in: Agronomy (Basel) Vol. 12; no. 8; p. 1901
• Main Authors: Han, Soon-Suk, Park, Hyun-Jin, Shin, Taehwan, Ko, Jonghan, Choi, Woo-Jung, Lee, Yun-Ho, Bae, Hui-Su, Ahn, Seung-Hyun, Youn, Jong-Tak, Kim, Han-Yong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Agricultural practices; Agricultural production; Analysis; Barley; Corn; Crop production; Crop yield; Crop yields; Crops; double-crop soybean; drained paddy fields; Experiments; Field tests; Floods; Flowering; growth; Moisture content; Monsoons; Phenology; Plowing; Rain; Rice; Rice fields; Seasons; Soil water; sowing date; Soybean; Soybeans; Study and teaching; Tillage; tillage systems; Volatility; Weather; Wind; Winter; South Korea; United States > US
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy12081901

In temperate monsoon areas, major constraints of soybean production in drained paddy fields are excess soil water during monsoon seasons. To further understand how agronomic practices and weather course affect the yield of soybeans, we conducted field experiments at Gwangju, Korea (35°10′ N, 126°53′ E) over three years (2018–2020). Double-crop soybeans were grown at two tillage systems (TS) [rotary tillage (RT), deep plowing followed by rotary tillage (DPRT)] and three sowing dates (SD) (June 10–15, June 25–30, and July 10–15) in drained paddy fields. Flowering phenology (R2) was accelerated by 5 days with each 15-day delay in SD. This resulted in a significant reduction in vegetative growth up to R2, with subsequent reductions in CGR and NAR through R2–R5 (beginning of grain filling). With a 30-day delay in SD, yield was significantly reduced by 27.0%. The better performance of RT over DPRT was demonstrated by greater yields (13.7%). In addition, yield was greatly varied with weather volatility among years, ranging from 123.8 to 552.0 g m−2. Weather volatility was the greatest contributor to yield variability (30.4%), followed by SD (17.0%) and TS (10.3%). Our results suggest that the yield might be mainly determined by how much growth has already been achieved before flowering and through R2–R5.