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Abstract Over the past century of maize (Zea mays L.) breeding, grain yield progress has been the result of improvements in several other intrinsic physiological and morphological traits. In this study, we describe (i) the contribution of kernel weight (KW) to yield genetic gain across multiple agronomic settings and breeding programs, and (ii) the physiological bases for improvements in KW for US hybrids. A global-scale literature review concludes that rates of KW improvement in US hybrids were similar to those of other commercial breeding programs but extended over a longer period of time. There is room for a continued increase of kernel size in maize for most of the genetic materials analysed, but the trade-off between kernel number and KW poses a challenge for future yield progress. Through phenotypic characterization of Pioneer Hi-Bred ERA hybrids in the USA, we determine that improvements in KW have been predominantly related to an extended kernel-filling duration. Likewise, crop improvement has conferred on modern hybrids greater KW plasticity, expressed as a better ability to respond to changes in assimilate availability. Our analysis of past trends and current state of development helps to identify candidate targets for future improvements in maize. 

For farmers, policymakers, and government agencies, it is critical to accurately define agricultural crop phenology and its spatial-temporal variability. At the moment, two approaches are utilized to report crop phenology. On one hand, land surface phenology provides information about the overall trend, whereas weekly reports from USDA-NASS provide information about the development of particular crops at the regional level. High-cadence earth observations might help to improve the accuracy of these estimations and bring more precise crop phenology classifications closer to what farmers demand. The second component of the proposed solution requires the use of robust classifiers (e.g., random forest, RF) capable of successfully managing large data sets. To evaluate this solution, this study compared the output of a RF classifier model using weather, two different satellite sources (Planet Fusion; PF and Sentinel-2; S-2), and ground truth data to improve maize (Zea mays L.) crop phenology classification using two regions of Kansas (Southwest and Central) as a testbed during the 2017 growing season. Our findings suggests that high temporal resolution (PF) data can significantly improve crop classification metrics (f1-score = 0.94) relative to S-2 (f1-score = 0.86). Additionally, a decline in the f1-score between 0.74 and 0.60 was obtained when we assessed the ability of S-2 to extend the temporal forecast for crop phenology. This research highlights the critical nature of very high temporal resolution (daily) earth observation data for crop monitoring and decision making in agriculture. 

Abstract Efficient, more accurate reporting of maize ( Zea mays L.) phenology, crop condition, and progress is crucial for agronomists and policy makers. Integration of satellite imagery with machine learning models has shown great potential to improve crop classification and facilitate in-season phenological reports. However, crop phenology classification precision must be substantially improved to transform data into actionable management decisions for farmers and agronomists. An integrated approach utilizing ground truth field data for maize crop phenology (2013–2018 seasons), satellite imagery (Landsat 8), and weather data was explored with the following objectives: (i) model training and validation—identify the best combination of spectral bands, vegetation indices (VIs), weather parameters, geolocation, and ground truth data, resulting in a model with the highest accuracy across years at each season segment (step one) and (ii) model testing—post-selection model performance evaluation for each phenology class with unseen data (hold-out cross-validation) (step two). The best model performance for classifying maize phenology was documented when VIs (NDVI, EVI, GCVI, NDWI, GVMI) and vapor pressure deficit (VPD) were used as input variables. This study supports the integration of field ground truth, satellite imagery, and weather data to classify maize crop phenology, thereby facilitating foundational decision making and agricultural interventions for the different members of the agricultural chain. 

Rapid increases in minimum night temperature than in maximum day temperature is predicted to continue, posing significant challenges to crop productivity. Rice and wheat are two major staples that are sensitive to high night‐temperature (HNT) stress. This review aims to (i) systematically compare the grain yield responses of rice and wheat exposed to HNT stress across scales, and (ii) understand the physiological and biochemical responses that affect grain yield and quality. To achieve this, we combined a synthesis of current literature on HNT effects on rice and wheat with information from a series of independent experiments we conducted across scales, using a common set of genetic materials to avoid confounding our findings with differences in genetic background. In addition, we explored HNT‐induced alterations in physiological mechanisms including carbon balance, source–sink metabolite changes and reactive oxygen species. Impacts of HNT on grain developmental dynamics focused on grain‐filling duration, post‐flowering senescence, changes in grain starch and protein composition, starch metabolism enzymes and chalk formation in rice grains are summarized. Finally, we highlight the need for high‐throughput field‐based phenotyping facilities for improved assessment of large‐diversity panels and mapping populations to aid breeding for increased resilience to HNT in crops.

 

Understanding the adaptive changes in wheat pollen lipidome under high temperature (HT) stress is critical to improving seed set and developing HT tolerant wheat varieties. We measured 89 pollen lipid species under optimum and high day and/or night temperatures using electrospray ionization‐tandem mass spectrometry in wheat plants. The pollen lipidome had a distinct composition compared with that of leaves. Unlike in leaves, 34:3 and 36:6 species dominated the composition of extraplastidic phospholipids in pollen under optimum and HT conditions. The most HT‐responsive lipids were extraplastidic phospholipids, phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol, phosphatidic acid, and phosphatidylserine. The unsaturation levels of the extraplastidic phospholipids decreased through the decreases in the levels of 18:3 and increases in the levels of 16:0, 18:0, 18:1, and 18:2 acyl chains. PC and PE were negatively correlated. Higher PC:PE at HT indicated possible PE‐to‐PC conversion, lower PE formation, or increased PE degradation, relative to PC. Correlation analysis revealed lipids experiencing coordinated metabolism under HT and confirmed the HT responsiveness of extraplastidic phospholipids. Comparison of the present results on wheat pollen with results of our previous research on wheat leaves suggests that similar lipid changes contribute to HT adaptation in both leaves and pollen, though the lipidomes have inherently distinct compositions.