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Chilling can decrease stomatal sensitivity to abscisic acid (ABA) in some legumes, although hormonal mechanisms involved are unclear. After evaluating leaf gas exchange of 16 European soybean genotypes at 14°C, 6 genotypes representing the range of response were selected. Further experiments combined low (L, 14°C) and high (H, 24°C) temperature exposure from sowing until the unifoliate leaf was visible and L or H temperature until full leaf expansion, to impose four temperature treatments: LL, LH, HL, and HH. Prolonged chilling (LL) substantially decreased leaf water content but increased leaf ethylene evolution and foliar concentrations of the ethylene precursor 1‐aminocyclopropane‐1‐carboxylic acid, indole‐3‐acetic acid, ABA and jasmonic acid. Across genotypes, photosynthesis linearly increased with stomatal conductance (Gs), with photosynthesis of HH plants threefold higher than LL plants at the same Gs. In all treatments except LL, Gs declined with foliar ABA accumulation. Foliar ABA sprays substantially decreased Gs of HH plants, but did not significantly affect LL plants. Thus low temperature compromised stomatal sensitivity to endogenous and exogenous ABA. Applying the ethylene antagonist 1 methyl‐cyclopropene partially reverted excessive stomatal opening of LL plants. Thus, chilling‐induced ethylene accumulation may mediate stomatal insensitivity to ABA, offering chemical opportunities for improving seedling survival in cold environments.

In the absence of stress, crop growth depends on the amount of light intercepted by the canopy and the conversion efficiency [radiation use efficiency (RUE)]. This study tested the hypothesis that long-term genetic gain for grain yield was partly due to improved RUE. The hypothesis was tested using 30 elite maize hybrids commercialized in the US corn belt between 1930 and 2017. Crops grown under irrigation showed that pre-flowering crop growth increased at a rate of 0.11 g m–2 year–1, while light interception remained constant. Therefore, RUE increased at a rate of 0.0049 g MJ–1 year–1, translating into an average of 3 g m–2 year–1 of grain yield over 100 years of maize breeding. Considering that the harvest index has not changed for crops grown at optimal density for the hybrid, the cumulative RUE increase over the history of commercial maize breeding in the USA can account for ~32% of the documented yield trend for maize grown in the central US corn belt. The remaining RUE gap between this study and theoretical maximum values suggests that a yield improvement of a similar magnitude could be achieved by further increasing RUE.

As the most produced grain crop in the world, maize (Zea mays) isa cornerstone of the global agricultural economy. Technological innovationsin molecular genetics, environmental characterization, and predictive breedinghave continued to drive genetic gain in maize, even for target populations ofenvironments with high heterogeneity of water availability. Environments proneto drought stress remain key targets where genetic gain must continue to maintain a resilient food supply. Here we review advances towards improving maize drought tolerance; the review focused on molecular and physiological mechanisms underpinning drought tolerance, and methodologies that improve prediction of the genotype by environment interactions under drought conditions.

Optimizing fertilizer rates is a common problem in modern agriculture. Frequently used response models ignore basic statistical assumptions and do not allow quantifying the effects of variables influencing yield response to fertilizer, generating uncertainty in fertilizer rate recommendations. We used linear mixed-effects models to explore maize (Zea mays L.) yield response to applied nitrogen (N) in late sowings, and we tested different predictors for explaining yield responses across sites. Data included yield response trials to applied N at 17 different environments (com-bination site × year) with four to five N rates replicated twice in each trial. The best model (Model A) that included significant effect of N rate applied, sowing date, and soil N-NO3 at sowing described grain yield variations with high accuracy (R2 = 0.93). Another best model (Model B) showed that soil type as additional variable affected significantly yield response to applied N. The final model indicated that the overall response across sites was characterized by a linear coefficient of 67 kg grain ha−1 per additional kg N ha−1 applied and a quadratic coefficient of −0.37 kg grain ha−1 per additional kg N ha−1 applied. Across all sites, soil N-NO3 at sowing (explored range from 34 to 356 kg N ha−1) explained 46% of the variability in the linear yield response to applied N. We proposed a method and generated statistical models with site specific covariates that can help optimize farm-ers’ decisions on the use of optimal N fertilizer rates. Fil: Coyos, Tomás. Aapresid; Argentina. Asociación Argentina de Productores en Siembra Directa; Argentina Fil: Borras, Lucas. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Cultivo Extensivos Cereales y Oleaginosas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina Fil: Gambin, Brenda Laura. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Cultivo Extensivos Cereales y Oleaginosas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina

Traditional breeding has been successful in delivering new cultivars with improved grain yield. This is particularly true in the case of maize, with global gains in yield always >2% since the massive introduction of hybrids in the 1960s. The advent of molecular tools in the 1980s, however, demanded an increased knowledge of the whole phenotype for enhancing the breadth of marker-assisted selection. This demand rose during the last decade, and revealed the existence of a large gap between genotypic and phenotypic knowledge, particularly for traits related to the physiological determinants of grain yield (i.e. traits conducive to biomass production and its partitioning). In this chapter, we use maize as a model crop for (1) reviewing general aspects of the physiological model of yield and its dissection in minor traits, (2) examining recent yield gains in major maize-producing countries, and breeding effects on the physiological determinants behind these gains, (3) discussing a field-based approach for phenotyping traits considered critical for yield determination at the crop level, and (4) analyzing possible genetic controls behind the response of yield to its physiological determinants. Opportunities and limitations to the application of field-based phenotyping in maize breeding are also addressed.

The influence of genotype and panicle position on sorghum [Sorghum bicolor (L.) Moench] kernel growth is poorly understood. In the present study, sorghum kernel weight (KW) differences during grain filling were analyzed by kernel water relationships previously described in other crops. Eight commercial genotypes differing in KW were used, and KW, water content, kernel volume, kernel moisture content, and kernel density were measured in two positions within the panicle (apical and basal) throughout the grain-filling period. At physiological maturity (PM), KW ranged from 16.5 to 25.1 mg kernel -1 , and a significant (p < 0.05) genotype x position interaction was detected. Independently of final KW, apical kernels always exhibited a higher rate (p < 0.001) and a shorter duration of grain filling (p < 0.001) than basal kernels. Maximum water content was related to kernel growth rate but not to final KW. Basal kernels reached maximum kernel volume after attaining maximum water content, with dry matter accumulation affecting kernel volume determination. Kernel density increased with a similar pattern regardless of genotype or panicle position when related to the kernel moisture decline, but at PM, basal kernels were always more dense than apical ones. Differences in the kernel desiccation pattern and in the critical moisture content for biomass accumulation helped explain differences in the grain-filling duration between positions. A general kernel growth curve based on kernel moisture content was impossible to obtain because of the differences in kernel growth patterns within the panicle.