Study of Factors Responsible for Abnormal Ear Development in Corn: A Regional Concern

In 2016, abnormal ear development was reported from several cornfields in Nebraska. In addition, well-substantiated reports extended from the Texas Panhandle to eastern Colorado and east through Kansas, Nebraska, Iowa, and Illinois. Similar issues occurred in Nebraska and other regions in subsequent years. Very little was known about what caused these abnormalities, challenging our understanding. Four projects phases studied abnormal ear’s underlying causes and their impact on yields. In phase one, two literature reviews were conducted to describe and summarize previously-reported symptoms, document the recent widespread symptoms of major concern, and study conditions potentially affecting corn ear formation, yield, and abnormal ears (Chapters 1, 2). In phase two, 15 surveys in South Central and Eastern Nebraska farmer fields characterized the 2016 reports (Chapter 3). In phase 3, experimental research was carried in 14 Nebraska distinct environments with different genetics and various management strategies to study the canopy level responses of abnormal ears (Chapters 4, 5). In phase four, morphological characteristics and plant yield differences between plants with normal and abnormal ears were studied at the plant level (Chapter 6). The overriding conclusions include 1) abnormal ears still plague cornfields, and it is essential to continue investigating the leading causes while identifying potential mitigation strategies; 2) abnormal ears are an observable limitation when trying to increase yields, depending on the frequency and severity of symptoms; 3) the selection of resistant hybrids and appropriate management are critical for crop adaptation, mitigation, and managing unfavorable conditions that can result in abnormal ears and lower yields; 4) plant morphological characteristics can help as diagnostic capacities to differentiate plants with normal and abnormal ears; 5) ear abnormalities should be understood as a result of the classic interaction among genetics (G), environment (E), and management (M). Fine-tuning G × E × M could help reduce and mitigate the likelihood of abnormal ears while increasing corn systems’ productivity, profitability, and sustainability

Study of nitrogen limitation and seed nitrogen sources for historical and modern genotypes in soybean

Master of ScienceDepartment of AgronomyIgnacio CiampittiSoybean [Glycine max (L.) Merr.] yields have continuously increased over time. Seed yields are determined by the genotype, environment, and management practices (G × E × M) interaction. Closing yield gaps require a continuous improvement in the use of the available resources, which must be attained via implementation of better management decisions. Linear relationships between seed yield and nitrogen (N) demand are reported in the scientific literature. Main sources of N to the plant are the biological N fixation (BNF) and the soil mineralization processes. On overall, only 50-60% of soybean N demand is met by the BNF process. An unanswered scientific knowledge is still related to the ability of the BNF to satisfy soybean N demand at varying yield levels. Seed N demand not met by N fixation plus soil mineral N, is then fulfilled by the remobilization of N from vegetative organs during the seed filling period. An early remobilization process reduces the photosynthetic activity (leaves) and can limit seed yield. The objectives of this project were to: i) study yield improvements and contribution of N via utilization of contrasting N conditions under historical and modern soybean genotypes, and ii) quantify main seed N sources during the seed filling period. For objective one, four field experiments were conducted during the 2016 and 2017 growing seasons in Kansas, United States (US) and Santa Fe Province, Argentina (ARG). Those experiments investigated twenty-one historical and modern soybean genotypes with release decades from 1980s to 2010s. As for objective two, three field experiments were conducted during the 2015 and 2016 growing seasons in Kansas, US, studying three soybean genotypes: non-roundup ready (RR), released in 1997; RR-1, released in 2009; and RR-2, released in 2014. Across all studies, seeds were inoculated and tested under three N management strategies: i) control without N application (Zero-N); ii) 56 kg N ha⁻¹ applied at reproductive growth stages (Late-N); and iii) 670 kg ha⁻¹ equally split at three timings (Full-N). As for yield improvements and N limitation, soybean yield improvements from the 1980s to 2010s were documented, representing 29% increases in the US and 21% in ARG. Regarding N management, the Full-N fertilization produced a 12% increase in seed yields in the US and 4% in ARG. As for main seed N sources in objective two, remobilization accounted for 59% of seed N demand, and was negatively related to new N uptake occurring during the seed filling period. Seed N demand for greater yields was dependent on both, N remobilization and new N uptake, while for lower yields, seed N demand was mainly supported by the N remobilization process. These results suggest that: a) high seed yields are somehow limited by the availability of N to express their potential, although the question about N application still remains to be fully investigated, as related to the timing and the environment by plant interactions that could promote a N limitation in soybeans; b) remobilization accounts for majority (59%) of N sourced to the seed, and c) high yielding soybean (modern genotypes) rely on diverse N sources: the N remobilization process plus new uptake of N

Refined teaching methods, systems thinking, and experiential approaches enhanced students learning through COVID-19

The Soil Nutrient Relationships course serves juniors and seniors with a major or minor in agronomy at the University of Nebraska-Lincoln. Pre-pandemic enrollment averaged 65 students. In 2021 and 2022, course enrollment was 42 and 55, respectively. The course was adjusted to a flipped design in 2017. Moving into 2021, the Soil Nutrient Relationships course underwent a major overhaul by changing the content source materials and organization of lab activities while maintaining the flipped delivery format. While responding to the COVID-19 pandemic limitations, the redesign was intended to focus limited face-to-face meetings (in person or webconference) on problem-solving activities. This paper reports on course redesign emphasizing changes for and since the pandemic. Surveys were used in both 2021 and 2022 to assess students’ learning and reception to the course design. In surveys, students responded that they gained knowledge in all course learning objectives and increased both problem-solving and systems approach skills. The overall responses were similar between 2021 and 2022; however, one difference was that students placed a higher value on the in-person discussion and lecture in 2022 relative to Zoom discussion or video lecture in 2021. Despite working on similar problem-solving activities, 81% responded that discussion helped with problem solving skills when done via Zoom in 2021 while 88% responded that in person discussion helped with problem-solving skills in 2022. Smaller group sizes used in 2021 seemed to improve student opinions of learning; this is the one change that instructors plan to use in the future

Abnormal ear development in corn: Does hybrid, environment, and seeding rate matter?

Corn (Zea mays L.) yields have increased in the United States since the 1930s and in other parts of the world since the 1950s and 1960s because of improvements in agricultural management and genotypes. Despite these increases, production concerns still exist. In July 2016, abnormal ear development (multi-ears per node, barbell-ears, and short-husks) was reported in cornfields that extended from the Texas Panhandle to eastern Colorado and east through Kansas, Nebraska, Iowa, and Illinois. Surveys in Nebraska farmer fields revealed significant productivity losses due to the issues, but little was known about the underlying causes. A research study was conducted in four Nebraska fields during the 2018 and 2019 growing seasons. The research investigated the effects of hybrids, environments, seeding rates, and their interactions on abnormal ears. Eight hybrids, eight environments, and five seeding rates were studied. About 63,500 plants were individually assessed at or after the dent stage (R5). Grain yield ranged from 4.3 to 20.1 Mg ha−1. In 2018, about 5% of ears were abnormal; in 2019, about 11%, if combined, about 8%. Higher-yielding hybrids were associated with lower percentages of abnormalities. Hybrids, environments, and seeding rates influenced the occurrence of abnormal ears. In most cases, abnormal ears had lower heights in the canopy, suggesting that primary ear loss may be a factor. The results reinforced the overriding hypothesis that ear abnormalities result from environmental, genetic, and management interactions. Depending on the environment, selecting certain hybrids with optimum seeding rates could help mitigate the occurrence of abnormal ears

Historical trend on seed amino acid concentration does not follow protein changes in soybeans

Abstract Soybean [Glycine max (L.) Merr.] is the most important oilseed crop for animal industry due to its high protein concentration and high relative abundance of essential and non-essential amino acids (AAs). However, the selection for high-yielding genotypes has reduced seed protein concentration over time, and little is known about its impact on AAs. The aim of this research was to determine the genetic shifts of seed composition for 18 AAs in 13 soybean genotypes released between 1980 and 2014. Additionally, we tested the effect of nitrogen (N) fertilization on protein and AAs trends. Soybean genotypes were grown in field conditions during two seasons under a control (0 N) and a N-fertilized treatment receiving 670 kg N ha−1. Seed yield increased 50% and protein decreased 1.2% comparing the oldest and newest genotypes. The application of N fertilizer did not significantly affect protein and AAs concentrations. Leucine, proline, cysteine, and tryptophan concentrations were not influenced by genotype. The other AAs concentrations showed linear rates of decrease over time ranging from − 0.021 to − 0.001 g kg−1 year−1. The shifts of 11 AAs (some essentials such as lysine, tryptophan, and threonine) displayed a relative-to-protein increasing concentration. These results provide a quantitative assessment of the trade-off between yield improvement and seed AAs concentrations and will enable future genetic yield gain without overlooking seed nutritional value

Corn response to long-term seasonal weather stressors: A review

Long-term weather patterns (environmental conditions or stresses exceeding 10 days in length) have the potential to influence corn (Zea mays L.) growth, development, and yield. This review summarizes the current knowledge (with emphasis placed on the Midwestern U.S. production environment) on how long-term weather conditions affect corn growth and yield, including (i) drought and heat stress, (ii) solar radiation, and (iii) distribution of heat unit accumulation during the season. Each section contains summaries of how these environmental factors influence corn growth and yield and provides context into past events experienced. The focus of the review is on dent corn grown for grain production, though relevant issues related to other types (i.e., silage corn) are included. This review also discusses agronomic recommendations or considerations to help alleviate the negative effects of stress conditions and identify areas where future research would be beneficial to continue improving the resiliency of corn cropping systems. Periods of high heat and water deficit as well as limited light availability challenge the ability to maximize yield production in corn. Temperature affects crop growth and development through the season, and accurately describing phenological progression using heat unit accumulation is a challenge. Advances in corn breeding and genetics, hybrid selection, and agronomic management practices will be key to ensuring long-range productivity and fully leveraging possible benefits from the shifts in long-range weather patterns.This article is published as Ortez, Osler A., Alexander J. Lindsey, Peter R. Thomison, Jeffrey A. Coulter, Maninder Pal Singh, Daniela R. Carrijo, Daniel J. Quinn, Mark A. Licht, and Leonardo Bastos. "Corn Response to Long‐Term Seasonal Weather Stressors: A Review." Crop Science (2023). doi:10.1002/csc2.21101. © 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

Estimating nitrogen, phosphorus, potassium, and sulfur uptake and requirement in soybean

Estimation of crop nutrient demand, seed nutrient removal, and nutrient use efficiency (yield to nutrient uptake ratio) are crucial for pursuing both balanced nutrition and more sustainable farming systems. However, the estimation of the nutrient requirements as the nutrient uptake per unit of seed yields is impaired in many situations due to the narrow variation of the dataset used to obtain these values or by the overgeneralization of considering a constant value for the nutrient demand at varying yield levels. Past studies focused on other crops and using linear models for estimation of the nutrient requirements, but not yet for soybeans (Glycine max L.). The aims of this research study were to: (i) quantify nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) requirements in soybean and (ii) compare linear and non-linear (spherical) models in their relationship between plant and seed nutrient content all relative to seed yield at varying probabilities utilizing quantile regression. A large dataset from different studies conducted between 2009–2018 period, including data of seed yield, total biomass at physiological maturity, and N, P, K, and S uptake. Soybean seed yield ranged from 955 to 6525 kg ha−1, aboveground biomass from 1990 to 15,814 kg ha−1, and harvest index from 0.16 to 0.57. On average, nutrient uptake was 261 kg N ha−1, 25 kg P ha−1, 133 kg K ha−1, and 16 kg S ha−1 (N:P:K:S ratio = 17:1.6:8.5:1), while nutrient content in seeds averaged 191 kg N ha−1, 17 kg P ha−1, 54 kg K ha−1, and 9 kg S ha−1 (N:P:K:S ratio = 21:1.8:5.8:1). The spherical model described better than the linear model the relationship between plant nutrient uptake or nutrient content in seeds with seed yield in soybean, and thus, nutrient requirements per unit of yield decreased as seed yield increased. A relationship between nutrient internal efficiency and seed yield for the different percentiles as determined by the non-linear quantile regression offered probabilistic values for estimating nutrient uptake in soybean, providing useful information for obtaining more reliable estimates of nutrient balances at the system-level.Fil: Salvagiotti, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Magnano, Luciana Ines. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Ortez, Osler. Universidad de Nebraska - Lincoln; Estados UnidosFil: Enrico, Juan Martín. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Barraco, Mirian Raquel. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Norte. Estacion Experimental Agropecuaria General Villegas. Agencia de Extension Rural General Villegas.; ArgentinaFil: Barbagelata, Pedro Aníbal. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; ArgentinaFil: Condori, Alicia Adelina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Di Mauro, Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentina. Corteva Agriscience; Estados UnidosFil: Manlla, Amalia Graciela. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Rotundo, José Luis. 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. Corteva Agriscience; Estados UnidosFil: Garcia, Fernando Oscar. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Ferrari, Manuel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; ArgentinaFil: Gudelj, Vicente. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Córdoba. Estación Experimental Agropecuaria Marcos Juárez; ArgentinaFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados Unido

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition

Objectives The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. Data description This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years