Drought and temperature limit tropical and temperate maize hybrids differently in a subtropical region

Abstract

Although the semiarid and subhumid Chaco regions in northern Argentina have been traditionally considered marginal and unsuitable for cultivating grain maize for human and livestock nutrition, this crop is increasingly being adopted by local farmers. The low maize yields observed in the area suggest that climatic constraints limit productivity, while changes in genotypes and management may be useful to mitigate the effect of these constraints. We analyzed data from 792 farm paddocks with multivariate mixed models to identify and quantify the main environmental and management constraints to maize's yield. In addition, we used the crop model CERES-Maize to assess the potential of a temperate maize hybrid to overcome water constraints. Results from the mixed models identified the amount of rainfall during February as a primary determinant of maize yield and showed that tropical hybrids tended to withstand higher temperatures and heat stress better, while temperate hybrids performed better under conditions of water scarcity. CERES-Maize simulations suggested that temperate maize hybrids have the potential to increase grain yields from 18 to 21 kg ha −1 (14.5% moisture content) for every millimeter of rain during February. This report is the first to identify alternative roles of temperate and tropical maize hybrids for counteracting climatic risks in the studied subtropical regions. These findings will provide plant breeders urgently needed information to breed better adapted maize genotypes for the semiarid and subhumid Chaco.

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

“…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.…”

Modeling maize and soybean responses to climatic change and soil degradation in a region of South America

“…Historical average annual precipitation is 692 mm (Angueira, Prieto, López, & Barraza, 2007), following a monsoonal pattern with higher precipitation in summer (December–March) than in winter (June–September). Summer crops are frequently exposed to temperatures above 30 °C during the late‐vegetative and reproductive stages, making them frequently affected by heat stress (Casali et al., 2018). Agricultural soils are mainly Haplustolls and Argiustolls.…”

“…The models CERES‐Maize and CROPGRO‐Soybean, which are part of DSSAT v4.5 (Hoogenboom et al., 2010), were used to assess the impact of climate change, soil degradation, and agricultural practices on grain yields of maize and soybean. The CERES‐Maize model has been calibrated in the study area for the cultivar DK747 in a previous report (Casali et al., 2018), whereas the CROPGRO‐Soybean model was calibrated for the cultivar A8000 as part of the present study. The dataset for soybean calibration was collected from field experiments managed by the National Agricultural Technological Institute located at La María (28°03′ S, 64°15′ W), Las Breñas (27°04′ S, 61°04′ W), and Charata (27°07′ S, 61°13′ W).…”

“…The Decision Support System for Agrotechnology Transfer (DSSAT) is a platform that simulates crop growth and development by integrating soil, climate, crop, and management submodels. The DSSAT models CERES‐Maize and CROPGRO‐Soybean have been successfully used to assess the impact of climate change and/or climate variability on agricultural productivity (e.g., Amouzou et al., 2019; Battisti et al., 2017; Ma et al., 2017) and were evaluated for different Argentine conditions with relatively low estimation errors (e.g., Casali et al., 2018; Caviglia, Sadras, & Andrade, 2013; Merlos et al., 2015). Although DSSAT models were originally developed in temperate regions, they have also been used in tropical and subtropical ones (Amouzou et al., 2019; Battisti et al., 2017; Liu et al., 2013).…”

Comparative analysis of two phytochrome mutants of tomato (Micro-Tom cv.) reveals specific physiological, biochemical, and molecular responses under chilling stress

“…Further, the resulting response of low temperature stress decreases the ability of energy utilization and thus causes the feedback inhibition of photosynthesis in plants [10]. The change in carbohydrate contents under low temperature has also been reported [11]. Moreover, low temperature could significantly affect sugar metabolism and endogenous hormone contents in plants [12].…”