Management options for reducing maize yield gaps in contrasting sowing dates

Abstract

Optimizing grain production implies defining the best management practices for a set of particular environments. Argentinean farmers in the central temperate region are sowing maize at two contrasting sowing dates (September to October and December), exposing their crops to very different growing environments. We tested the influence of management and environmental variables affecting maize yield at early (ES) or late (LS) sowings. Our objectives were to (i) determine the most relevant management and environmental yield predictors for ES and LS, (ii) quantify the magnitude of their effects, and (iii) explore potential yield increments after optimizing crop management within each sowing. We conducted 91 on-farm multi-environment trials during six years (2010)(2011)(2012)(2013)(2014)(2015)(2016) around the central temperate region, and analyzed 13 management and environmental variables.The most relevant environmental predictors (relative importance > 0.50) for both sowing dates included presence of a water table at sowing, rainfall during the crop cycle, and their interactions. Presence of a water table had a positive or negative effect for ES or LS, respectively. Management yield predictors varied depending on the sowing date. Stand density, N and S availability were important yield predictors at ES, while fungicide use, soil P, and N availability were the most relevant ones at LS. Farmers can increase yield at each sowing date by optimizing these management practices. Optimizing stand density and N availability in ES can have a ∼3,053 kg ha −1 effect, while fungicide use in LS can increase yield by ∼1040 kg ha −1 . Determining the adequate sowing date based on the presence of a water table at sowing can have a ∼1000 kg ha −1 effect. Our results described specific management options for reducing yield gaps and optimize maize production across contrasting sowing dates.Argentinean farmers in the central temperate region are sowing their maize crops at two contrasting dates. Early sowing dates (ES) take place during September-October, while late sowing dates (LS) take place around December. While ES is traditional for the region, LS became important more recently following the introduction of insect resistant (i.e., Bt) corn in late 1990s. These contrasting sowing dates expose the crop to different scenarios in terms of temperature, solar radiation, and water availability (Maddonni, 2012). Late sowing dates locate the critical flowering period for yield definition (Andrade et al., 1999) under conditions of higher rainfall probability, lower evaporative

Maize nitrogen management in soils with influencing water tables within optimum depth

“…This rise impacted crop water availability, adding, in some cases, >300 mm of water (i.e., half of maize water requirements) (Portela et al, 2009) and helping achieve high and more stable yields when water table fluctuates around optimum levels (1.4-2.45 m depth; Nosetto et al, 2009;Rizzo et al, 2018). However, local studies also showed that shallow groundwater levels can have negative effects on maize yields under high rainfall levels (Vitantonio-Mazzini et al, 2020), especially when soil water tables are <1.4 m depth from soil surface, causing roots and plant death, salinization, and N loses (Nosetto et al, 2009;. Maize yield response to N rate depends on attainable yield and on the intrinsic capacity of the soil to deliver N (Salvagiotti et al, 2011).…”

Sowing date, genotype choice, and water environment control soybean yields in central Argentina

“…Data were analyzed using linear mixed-effects models to assess the influence of different predictors on grain yield (lme4 package, lmer function; Bates, Maechler, Bolker, & Walker, 2015). We applied the top-down strategy for the model selection process (Coyos et al, 2018;Gambin et al, 2016;Vitantonio-Mazzini et al, 2020;Zuur et al, 2009). After data exploration, we proposed a 'beyond-optimal model' which included different variables that presented association with grain yield.…”

“…To explore the impact of multiple and potentially interactive management and environmental variables on soybean grain yield across the central Argentinean temperate region, we conducted 53 field trials in farmer fields during three consecutive cropping years. We used linear mixed‐effects models and multi‐model inference (MMI) techniques (Burnham & Anderson, 2004; Smith, Cullis, & Thompson, 2005) as they proved to be particularly useful to quantify multiple variable effects from experimental agricultural data (Casali, Rubio, & Herrera, 2018; Gambin, Coyos, Di Mauro, Borrás, & Garibaldi, 2016; Vitantonio‐Mazzini et al., 2020). We hypothesized that the most relevant management variables are sowing date and genotype selection, while water table presence and rainfall during the crop cycle are the most important environmental controls.…”

Impact of increased temperature on spring wheat yield in northern China

“…Simulation models have been employed to demonstrate the negative effects of high temperature on spring wheat productivity in the cool regions (Xiao et al, 2017; Zhao et al, 2017). However, simulating sometimes is prone to errors and results always have a level of uncertainty (Vitantonio‐Mazzini et al, 2020). Inversely, other studies have shown that increased temperature in these regions improved wheat yield (He et al, 2020; Xiao et al, 2008).…”