Can Changes in Canopy and/or Root System Architecture Explain Historical Maize Yield Trends in the U.S. Corn Belt?

Characterization of flag leaf morphology identifies a major genomic region controlling flag leaf angle in the US winter wheat (Triticum aestivum L.)

KEY MESSAGE

Multi-environmental characterization of flag leaf morphology traits in the US winter wheat revealed nine stable genomic regions for different flag leaf-related traits including a major region governing flag leaf angle. Flag leaf in wheat is the primary contributor to accumulating photosynthetic assimilates. Flag leaf morphology (FLM) traits determine the overall canopy structure and capacity to intercept the light, thus influencing photosynthetic efficiency. Hence, understanding the genetic control of these traits could be useful for breeding desirable ideotypes in wheat. We used a panel of 272 accessions from the hard winter wheat (HWW) region of the USA to investigate the genetic architecture of five FLM traits including flag leaf length (FLL), width (FLW), angle (FLANG), length-width ratio, and area using multilocation field experiments. Multi-environment GWAS using 14,537 single-nucleotide polymorphisms identified 36 marker-trait associations for different traits, with nine being stable across environments. A novel and major stable region for FLANG (qFLANG.1A) was identified on chromosome 1A accounting for 9-13% variation. Analysis of spatial distribution for qFLANG.1A in a set of 2354 breeding lines from the HWW region showed a higher frequency of allele associated with narrow leaf angle. A KASP assay was developed for allelic discrimination of qFLANG.1A and was used for its independent validation in a diverse set of spring wheat accessions. Furthermore, candidate gene analysis for two regions associated with FLANG identified seven putative genes of interest for each of the two regions. The present study enhances our understanding of the genetic control of FLM in wheat, particularly FLANG, and these results will be useful for dissecting the genes underlying canopy architecture in wheat facilitating the development of climate-resilient wheat varieties.

Multi-scale characterisation of cold response reveals immediate and long-term impacts on cell physiology up to seed composition in sunflower

Early sowing can help summer crops escape drought and can mitigate the impacts of climate change on them. However, it exposes them to cold stress during initial developmental stages, which has both immediate and long-term effects on development and physiology. To understand how early night-chilling stress impacts plant development and yield, we studied the reference sunflower line XRQ under controlled, semi-controlled and field conditions. We performed high-throughput imaging of the whole plant parts and obtained physiological and transcriptomic data from leaves, hypocotyls and roots. We observed morphological reductions in early stages under field and controlled conditions, with a decrease in root development, an increase in reactive oxygen species content in leaves and changes in lipid composition in hypocotyls. A long-term increase in leaf chlorophyll suggests a stress memory mechanism that was supported by transcriptomic induction of histone coding genes. We highlighted DEGs related to cold acclimation such as chaperone, heat shock and late embryogenesis abundant proteins. We identified genes in hypocotyls involved in lipid, cutin, suberin and phenylalanine ammonia lyase biosynthesis and ROS scavenging. This comprehensive study describes new phenotyping methods and candidate genes to understand phenotypic plasticity better in response to chilling and study stress memory in sunflower.

Root proliferation adaptation strategy improved maize productivity in the US Great Plains: Insights from crop simulation model under future climate change

Adaptation measures are essential for reducing the impact of future climate risks on agricultural production systems. The present study focuses on implementing an adaptation strategy to mitigate the impact of future climate change on rainfed maize production in the Eastern Kansas River Basin (EKSRB), an important rainfed maize-producing region in the US Great Plains, which faces potential challenges of future climate risks due to a significant east-to-west aridity gradient. We used a calibrated CERES-Maize crop model to evaluate the impacts of baseline climate conditions (1985-2014), late-term future climate scenarios (under the SSP245 emission pathway and CMIP6 models), and a novel root proliferation adaptation strategy on regional maize yield and rainfall productivity. Changes in the plant root system by increasing the root density could lead to yield benefits, especially under drought conditions. Therefore, we modified the governing equation of soil root growth in the CERES-Maize model to reflect the genetic influence of a maize cultivar to improve root density by proliferation. Under baseline conditions, maize yield values ranged from 6522 to 12,849 kgha-1, with a regional average value of 9270 kgha-1. Projections for the late-term scenario indicate a substantial decline in maize yield (36 % to 50 %) and rainfall productivity (25 % to 42 %). Introducing a hypothetical maize cultivar by employing root proliferation as an adaptation strategy resulted in a 27 % increase in regional maize yield, and a 28 % increase in rainfall productivity compared to the reference cultivar without adaptation. We observed an indication of spatial dependency of maize yield and rainfall productivity on the regional precipitation gradient, with counties towards the east having an implicit advantage over those in the west. These findings offer valuable insights for the US Great Plains maize growers and breeders, guiding strategic decisions to adapt rainfed maize production to the region's impending challenges posed by climate change.

Reduced tillering and dwarfing genes alter root traits and rhizo-economics in wheat

The tiller inhibition (tin) and Reduced height (Rht) genes strongly influence the carbon partitioning and architecture of wheat shoots, but their effects on the energy economy of roots have not been examined in detail. We examined multiple root traits in three sets of near-isogenic wheat lines (NILs) that differ in the tin gene or various dwarfing gene alleles (Rht-B1b, Rht-D1b, Rht-B1c and Rht-B1b + Rht-D1b) to determine their effects on root structure, anatomy and carbon allocation. The tin gene resulted in fewer tillers but more costly roots in an extreme tin phenotype with a Banks genetic background due to increases in root-to-shoot ratio, total root length, and whole root respiration. However, this effect depended on the genetic background as tin caused both smaller shoots and roots in a different genetic background. The semi-dwarf gene Rht-B1b caused few changes to the root structure, whereas Rht-D1b, Rht-B1c and the double dwarf (Rht-B1b + Rht-D1b) decreased the root biomass. Rht-B1c reduced the energy cost of roots by increasing specific root length, increasing the volume of cortical aerenchyma and by reducing root length, number, and biomass without affecting the root-to-shoot ratio. This work informs researchers using tin and Rht genes how to modify root system architecture to suit specific environments.

Genetic regulation of self-organizing azimuthal canopy orientations and their impacts on light interception in maize

The efficiency of solar radiation interception contributes to the photosynthetic efficiency of crop plants. Light interception is a function of canopy architecture, including plant density; leaf number, length, width, and angle; and azimuthal canopy orientation. We report on the ability of some maize (Zea mays) genotypes to alter the orientations of their leaves during development in coordination with adjacent plants. Although the upper canopies of these genotypes retain the typical alternate-distichous phyllotaxy of maize, their leaves grow parallel to those of adjacent plants. A genome-wide association study (GWAS) on this parallel canopy trait identified candidate genes, many of which are associated with shade avoidance syndrome, including phytochromeC2. GWAS conducted on the fraction of photosynthetically active radiation (PAR) intercepted by canopies also identified multiple candidate genes, including liguleless1 (lg1), previously defined by its role in ligule development. Under high plant densities, mutants of shade avoidance syndrome and liguleless genes (lg1, lg2, and Lg3) exhibit altered canopy patterns, viz, the numbers of interrow leaves are greatly reduced as compared to those of nonmutant controls, resulting in dramatically decreased PAR interception. In at least the case of lg2, this phenotype is not a consequence of abnormal ligule development. Instead, liguleless gene functions are required for normal light responses, including azimuth canopy re-orientation.

Diverse tillage practices with straw mulched management strategies to improve water use efficiency and maize productivity under a dryland farming system

Straw mulching incorporation has a wide range of environmental benefits that make it an effective practice for sustainable agro-ecosystem in the semi-arid regions. There is an urgent need to improve the 13C-photosynthates distribution, water use efficiency (WUE) and maize canopy characteristics under the diverse tillage practices with straw mulched management strategies for sustainable intensification of maize production. The field study consists of three diverse tillage systems (RT: rotary tillage; CT, conventional tillage; MT, minimum tillage) with three straws mulching (NS: no straw mulch; SS: straw mulch on the soil surface; SI: straw incorporated into the soil) were assessed under the ridge-furrow rainfall harvesting system. Our results showed that the rotary tillage with straw incorporated into the soil significantly reduces the ET rate (11 %), and leaf rolling index; as a result considerably improves LAI, LEI, 13C-photosynthates distribution, N accumulation, and above ground biomass under various growth stages. The RTSI treatment significantly improved soil water storage, soil organic carbon (52 %, SOC), soil C storage (39 %, SCS), and NPK nutrients uptake (70 %, 62 %, and 69 %) of maize than observed for the rest of all other treatments, respectively. The RTSI treatment improves soil water balance, grain yield (53 %), biomass yield (37 %), WUEg (51 %), WUEb (35 %), nutrients uptake, and mitigating soil water depletion than the MTNS treatment. Although RTSS can achieve optimal soil water storage in the short term, RTSI has a great potential in improving soil carbon stability, canopy characteristics, soil water storage, and WUE, contributing to sustainable and intensive corn production in agricultural ecosystems in semi-arid regions.

Genetic mapping of dynamic control of leaf angle across multiple canopy levels in maize

Optimizing leaf angle and other canopy architecture traits has helped modern maize (Zea mays L.) become adapted to higher planting densities over the last 60 years. Traditional investigations into genetic control of leaf angle have focused on one leaf or the average of multiple leaves; as a result, our understanding of genetic control across multiple canopy levels is still limited. To address this, genetic mapping across four canopy levels was conducted in the present study to investigate the genetic control of leaf angle across the canopy. We developed two populations of doubled haploid lines derived from three inbreds with distinct leaf angle phenotypes. These populations were genotyped with genotyping-by-sequencing and phenotyped for leaf angle at four different canopy levels over multiple years. To understand how leaf angle changes across the canopy, the four measurements were used to derive three additional traits. Composite interval mapping was conducted with the leaf-specific measurements and the derived traits. A set of 59 quantitative trait loci (QTLs) were uncovered for seven traits, and two genomic regions were consistently detected across multiple canopy levels. Additionally, seven genomic regions were found to contain consistent QTLs with either relatively stable or dynamic effects at different canopy levels. Prioritizing the selection of QTLs with dynamic effects across the canopy will aid breeders in selecting maize hybrids with the ideal canopy architecture that continues to maximize yield on a per area basis under increasing planting densities.

Can high-yielding maize system decrease greenhouse gas emissions largely while simultaneously enhancing economic and ecosystem benefits through the "Rhizobiont" concept? Evidence from field

Ensuring high grain yields while minimizing environmental costs is a pressing imperative aligned with the Sustainable Development Goals (SDGs). In this study, we sought to establish a high-yielding maize system (HYMS) by implementing the innovative "Rhizobiont" concept for nutrient management, while substantially reducing greenhouse gas emissions. A 2-yr field study was conducted in a station of China Agriculture University (Wuqiao) with six treatments. The HYMS was established to achieve a harmonious equilibrium among genetic factors, environmental conditions, and management practices. HYMS demonstrated a significant boost in grain yield, averaging 12,706.6 kg ha-1 in 2021 and 13,676.4 kg ha-1 in 2022. These represented substantial increases of 25.6 % and 25.5 %, respectively, when compared to the current farmers practices (CP). More importantly, the N rate in HYMS was optimized to 148.2 kg ha-1 in 2021 and 138.0 kg ha-1 in 2022 with the implementation of the "Rhizobiont" concept. This represented a remarkable reduction of 35.5 % to 39.9 % in N application compared to CP. As a direct consequence, the measured cumulative emissions of greenhouse gases such as CO2, N2O, and CH4 in HYMS were notably decreased, showing reductions of 24.1 %, 36.0 %, and 7.0 %, respectively, compared to CP. Furthermore, the carbon intensity in HYMS was significantly reduced by 43.7 %. These considerable reductions in fertilizer use translated into tangible economic benefits (EB) and ecosystem economic benefit (EEB) in HYMS. EB was found to be 90.9 % higher, while EEB was 117.9 % higher than CP. These findings underscore the vast potential of HYMS and the "Rhizobiont" concept in promoting sustainable agriculture, with far-reaching implications for global food security and the well-being of smallholder farmers.

Prospecting the Potential of Plant Growth-Promoting Microorganisms for Mitigating Drought Stress in Crop Plants

Drought is a global phenomenon affecting plant growth and productivity, the severity of which has impacts around the whole world. A number of approaches, such as agronomic, conventional breeding, and genetic engineering, are followed to increase drought resilience; however, they are often time consuming and non-sustainable. Plant growth-promoting microorganisms are used worldwide to mitigate drought stress in crop plants. These microorganisms exhibit multifarious traits, which not only help in improving plant and soil health, but also demonstrate capabilities in ameliorating drought stress. The present review highlights various adaptive strategies shown by these microbes in improving drought resilience, such as modulation of various growth hormones and osmoprotectant levels, modification of root morphology, exopolysaccharide production, and prevention of oxidative damage. Gene expression patterns providing an adaptive edge for further amelioration of drought stress have also been studied in detail. Furthermore, the practical applications of these microorganisms in soil are highlighted, emphasizing their potential to increase crop productivity without compromising long-term soil health. This review provides a comprehensive coverage of plant growth-promoting microorganisms-mediated drought mitigation strategies, insights into gene expression patterns, and practical applications, while also guiding future research directions.

Strategies of Molecular Signal Integration for Optimized Plant Acclimation to Stress Combinations

Plant growth and survival in their natural environment require versatile mitigation of diverse threats. The task is especially challenging due to the largely unpredictable interaction of countless abiotic and biotic factors. To resist an unfavorable environment, plants have evolved diverse sensing, signaling, and adaptive molecular mechanisms. Recent stress studies have identified molecular elements like secondary messengers (ROS, Ca2+, etc.), hormones (ABA, JA, etc.), and signaling proteins (SnRK, MAPK, etc.). However, major gaps remain in understanding the interaction between these pathways, and in particular under conditions of stress combinations. Here, we highlight the challenge of defining "stress" in such complex natural scenarios. Therefore, defining stress hallmarks for different combinations is crucial. We discuss three examples of robust and dynamic plant acclimation systems, outlining specific plant responses to complex stress overlaps. (a) The high plasticity of root system architecture is a decisive feature in sustainable crop development in times of global climate change. (b) Similarly, broad sensory abilities and apparent control of cellular metabolism under adverse conditions through retrograde signaling make chloroplasts an ideal hub. Functional specificity of the chloroplast-associated molecular patterns (ChAMPs) under combined stresses needs further focus. (c) The molecular integration of several hormonal signaling pathways, which bring together all cellular information to initiate the adaptive changes, needs resolving.

Crop/Plant Modeling Supports Plant Breeding: I. Optimization of Environmental Factors in Accelerating Crop Growth and Development for Speed Breeding

The environmental conditions in customered speed breeding practice are, to some extent, empirical and, thus, can be further optimized. Crop and plant models have been developed as powerful tools in predicting growth and development under various environments for extensive crop species. To improve speed breeding, crop models can be used to predict the phenotypes resulted from genotype by environment by management at the population level, while plant models can be used to examine 3-dimensional plant architectural development by microenvironments at the organ level. By justifying the simulations via numerous virtual trials using models in testing genotype × environment × management, an optimized combination of environmental factors in achieving desired plant phenotypes can be quickly determined. Artificial intelligence in assisting for optimization is also discussed. We admit that the appropriate modifications on modeling algorithms or adding new modules may be necessary in optimizing speed breeding for specific uses. Overall, this review demonstrates that crop and plant models are promising tools in providing the optimized combinations of environment factors in advancing crop growth and development for speed breeding.

Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize

We review approaches to maize breeding for improved drought tolerance during flowering and grain filling in the central and western US corn belt and place our findings in the context of results from public breeding. Here we show that after two decades of dedicated breeding efforts, the rate of crop improvement under drought increased from 6.2 g m-2 year-1 to 7.5 g m-2 year-1, closing the genetic gain gap with respect to the 8.6 g m-2 year-1 observed under water-sufficient conditions. The improvement relative to the long-term genetic gain was possible by harnessing favourable alleles for physiological traits available in the reference population of genotypes. Experimentation in managed stress environments that maximized the genetic correlation with target environments was key for breeders to identify and select for these alleles. We also show that the embedding of physiological understanding within genomic selection methods via crop growth models can hasten genetic gain under drought. We estimate a prediction accuracy differential (Δr) above current prediction approaches of ~30% (Δr=0.11, r=0.38), which increases with increasing complexity of the trait environment system as estimated by Shannon information theory. We propose this framework to inform breeding strategies for drought stress across geographies and crops.

Comparative transcriptome profiling to unravel the key molecular signalling pathways and drought adaptive plasticity in shoot borne root system of sugarcane

Sugarcane root system comprises of superficial sett roots as well as deeply-penetrating shoot borne roots (SBR) with latter being the permanent root system. In sugarcane, the healthy SBR contributes to a better crop yield and it also helps to produce multiple ratoon crops after the harvest. There is a dearth of in-depth knowledge on SBR system architecture and its functional role in modern day commercial hybrids. A comprehensive phenotypic, anatomical and whole transcriptome profiling, conducted between the commercial sugarcane hybrids and a wild germplasm Erianthus, found a developmental delay in both initiation and establishment of the SBR in commercial hybrid compared to Erianthus. The SBR system in Erianthus proved to be an extensive drought-adaptive root system architecture that significantly contributes to drought tolerance. On the other hand, SBRs in the commercial hybrids showed an irreversible collapse and damage of the root cells under drought stress. The outcomes from the comparative analysis of the transcriptome data showed a significant upregulation of the genes that regulate important stress signalling pathways viz., sugar, calcium, hormone signalling and phenylpropanoid biosynthesis in the SBRs of Erianthus. It was found that through these key signalling pathways, Erianthus SBRs triggered the downstream signalling cascade to impart physiological responses like osmoprotection, modification of the cell walls, detoxification of reactive oxygen species, expression of drought responsive transcription factors, maintenance of cell stability and lateral root development. The current study forms a basis for further exploration of the Shoot Borne Root system as a valuable breeding target to develop drought tolerant sugarcane genotypes.

Early root phenotyping in sweetpotato (Ipomoea batatas L.) uncovers insights into root system architecture variability

Background

We developed a novel, non-destructive, expandable, ebb and flow soilless phenotyping system to deliver a capable way to study early root system architectural traits in stem-derived adventitious roots of sweetpotato (Ipomoea batatas L.). The platform was designed to accommodate up to 12 stems in a relatively small area for root screening. This platform was designed with inexpensive materials and equipped with an automatic watering system.

Methods

To test this platform, we designed a screening experiment for root traits using two contrasting sweetpotato genotypes, 'Covington' and 'NC10-275'. We monitored and imaged root growth, architecture, and branching patterns every five days up to 20 days.

Results

We observed significant differences in both architectural and morphological root traits for both genotypes tested. After 10 days, root length, surface root area, and root volume were higher in 'NC10-275' compared to 'Covington'. However, average root diameter and root branching density were higher in 'Covington'.

Conclusion

These results validated the effective and efficient use of this novel root phenotyping platforming for screening root traits in early stem-derived adventitious roots. This platform allowed for monitoring and 2D imaging of root growth over time with minimal disturbance and no destructive root sampling. This platform can be easily tailored for abiotic stress experiments, and permit root growth mapping and temporal and dynamic root measurements of primary and secondary adventitious roots. This phenotyping platform can be a suitable tool for examining root system architecture and traits of clonally propagated material for a large set of replicates in a relatively small space.

Natural variation of maize root hydraulic architecture underlies highly diverse water uptake capacities

Plant water uptake is determined by the root system architecture and its hydraulic capacity, which together define the root hydraulic architecture. The current research aims at understanding the water uptake capacities of maize (Zea mays), a model organism and major crop. We explored the genetic variations within a collection of 224 maize inbred Dent lines and successively defined core genotype subsets to access multiple architectural, anatomical, and hydraulic parameters in the primary root (PR) and seminal roots (SR) of hydroponically grown seedlings. We found 9-, 3.5-, and 12.4-fold genotypic differences for root hydraulics (Lpr), PR size, and lateral root size, respectively, that shaped wide and independent variations of root structure and function. Within genotypes, PR and SR showed similarities in hydraulics and, to a lesser extent, in anatomy. They had comparable aquaporin activity profiles that, however, could not be explained by aquaporin expression levels. Genotypic variations in the size and number of late meta xylem vessels were positively correlated with Lpr. Inverse modeling further revealed dramatic genotypic differences in the xylem conductance profile. Thus, tremendous natural variation of maize root hydraulic architecture underlies a high diversity of water uptake strategies and paves the way to quantitative genetic dissection of its elementary traits.

Developing Functional Relationships between Soil Moisture Content and Corn Early-Season Physiology, Growth, and Development

Drought is a severe threat to agriculture production that affects all growth stages of plants, including corn (Zea mays L.). Any factor affecting early seedling growth and development will significantly impact yield. Despite the recurrence of low rainfall during the growing seasons, corn responses to different early-season soil moisture content levels have not been investigated. In this study, we investigated how corn morpho-physiological and biomass traits responded to varied soil moisture content during the early vegetative stage. Two corn hybrids were grown in a pot-culture facility under five different soil moisture treatments (0.15, 0.12, 0.09, 0.06, and 0.03 m³ m^-3 volumetric water content, VWC) to assess the growth and developmental responses to varied soil moisture content during early-season growth (V2 to V7) stage. Sub-optimal soil moisture content limited plant growth and development by reducing physiological and phenotypic expression. Stomatal conductance and transpiration were decreased by an average of 65% and 59% across stress treatments relative to optimum conditions. On average, soil moisture deficit reduced the total leaf area by 71% and 72% compared to the control in 'A6659VT2RIB' and 'P1316YHR', respectively. Shoot and root dry weights were reduced by 74% and 43% under 0.03 m³ m^-3 VWC. An increase in the root-to-shoot ratio was noticed under low VWC conditions compared to the control. Based on the stress tolerance index, the physiology and leaf growth parameters were more sensitive to soil moisture deficit. Our results highlight the impact of sub-optimal soil moisture on physiology and morphological traits during early-season growth. 'P1316YHR' demonstrated better physiological performance under stress conditions, while 'A6659VT2RIB' produced relatively better root growth. The findings suggest that biomass partitioning between shoot and root components is dynamic and depends on stress intensity. The current findings can help to prioritize traits associated with the early-season drought tolerance in corn. The functional relationships developed between soil moisture content and growth and developmental responses can be integrated into corn crop modeling to allow better irrigation management decisions.

Genomic insight into changes of root architecture under drought stress in maize

Drought stress is a central environmental factor that severely limits maize production worldwide. Root architecture plays an important role in drought tolerance and can be targeted in breeding programmes. Here, we conducted phenotyping of root architecture under different water treatments for 373 maize inbred lines, representative germplasm from both China and the United States in different breeding eras. We found that seminal root length in response to drought stress experienced convergent increase during breeding in both countries. Using a genome-wide association study, we identified a total of 221 associated loci underlying 13 root traits under well-watered and water-stressed conditions. These loci harboured many reported root- and abiotic stress-related genes. Furthermore, a total of 75 strong candidate genes were prioritised by integrating candidate genes associated with seminal root length and differentially expressed genes in seminal root. One of high-confidence candidate genes, ZmCIPK3 was functionally characterised and probably plays a role in enhancing drought tolerance through regulating seminal root growth. This study provides valuable information for genetic improvement of root architecture and drought tolerance in maize.

Mining genes regulating root system architecture in maize based on data integration analysis

KEY MESSAGE

A new strategy that integrated multiple public data resources was established to construct root gene co-expression network and mine genes regulating root system architecture in maize. A root gene co-expression network, containing 13,874 genes, was constructed. A total of 53 root hub genes and 16 priority root candidate genes were identified. One priority root candidate was further functionally verified using overexpression transgenic maize lines. Root system architecture (RSA) is crucial for crops productivity and stress tolerance. In maize, few RSA genes are functionally cloned, and effective discovery of RSA genes remains a great of challenge. In this work, we established a strategy to mine maize RSA genes by integrating functionally characterized root genes, root transcriptome, weighted gene co-expression network analysis (WGCNA) and genome-wide association analysis (GWAS) of RSA traits based on public data resources. A total of 589 maize root genes were collected by searching well-characterized root genes in maize or homologous genes of other species. We performed WGCNA to construct a maize root gene co-expression network containing 13874 genes based on public available root transcriptome data, and further discovered the 53 hub genes related to root traits. In addition, by the prediction function of obtained root gene co-expression network, a total of 1082 new root candidate genes were explored. By further overlapping the obtained new root candidate gene with the root-related GWAS of RSA candidate genes, 16 priority root candidate genes were identified. Finally, a priority root candidate gene, Zm00001d023379 (encodes pyruvate kinase 2), was validated to modulate root open angle and shoot-borne roots number using its overexpression transgenic lines. Our results develop an integration analysis method for effectively exploring regulatory genes of RSA in maize and open a new avenue to mine the candidate genes underlying complex traits.

Variation in the Root System Architecture of Peach × (Peach × Almond) Backcrosses

The spatial arrangement and growth pattern of root systems, defined by the root system architecture (RSA), influences plant productivity and adaptation to soil environments, playing an important role in sustainable horticulture. Florida's peach production area covers contrasting soil types, making it necessary to identify rootstocks that exhibit soil-type-specific advantageous root traits. In this sense, the wide genetic diversity of the Prunus genus allows the breeding of rootstock genotypes with contrasting root traits. The evaluation of root traits expressed in young seedlings and plantlets facilitates the early selection of desirable phenotypes in rootstock breeding. Plantlets from three peach × (peach × almond) backcross populations were vegetatively propagated and grown in rhizoboxes. These backcross populations were identified as BC1251, BC1256, and BC1260 and studied in a completely randomized design. Scanned images of the entire root systems of the plantlets were analyzed for total root length distribution by diameter classes, root dry weight by depth horizons, root morphological components, structural root parameters, and root spreading angles. The BC1260 progeny presented a shallower root system and lower root growth. Backcross BC1251 progeny exhibited a more vigorous and deeper root system at narrower root angles, potentially allowing it to explore and exploit water and nutrients in deep sandy entisols from the Florida central ridge.

Root plasticity: an effective selection technique for identification of drought tolerant maize (Zea mays L.) inbred lines

The decline in tropical maize productivity due to climatic vulnerability is a matter of serious concern as being a food and feed/fodder commodity, it is an important crop for the sustenance of human life. Genetic selections and development of water deficit stress (WDS) tolerant commercial varieties have potential to offset the impact of changing temperatures and precipitation. For trait-specific genetic enhancement, there is a need to understand a suite of adaptation strategies for crop. We studied the response of various shoot and root traits in 71 maize inbreds of diverse origin under simulated sub-optimal water supply controlled conditions, delineated an array of traits which must be considered for selection for WDS and validated the inbreds harbouring tolerance to WDS for selection of authentic donor lines to develop WDS tolerant hybrids. A large data set was limited to uncorrelated traits based on principal component analysis and variability among maize lines was deciphered using heatmap dendrogram. We also reported the relevance of root anatomical plasticity to the inherent potential of lines to combat WDS. We recommend incorporating the changes in number and diameter of xylem and metaxylem under simulated controlled conditions as a part of precise phenotyping for WDS in maize. The study led to identification of WDS tolerant line LM22 in maize.

Alleviation of Associated Drought and Salinity Stress' Detrimental Impacts on an Eggplant Cultivar ('Bonica F1') by Adding Biochar

To investigate the impact of biochar on eggplant growth, physiology, and yield parameters under separate and associated drought and salt stress, a pot experiment was carried out. An eggplant variety ('Bonica F1') was exposed to one NaCl concentration (S1 = 300 mM), three irrigation regimes (FI: full irrigation; DI: deficit irrigation; ARD: alternate root-zone drying irrigation), and one dose of biochar (B1 = 6% by weight). Our findings demonstrated that associated drought and salt stress had a greater negative impact on 'Bonica F1' performance in comparison to single drought or salt stress. Whereas, adding biochar to the soil improved the ability of 'Bonica F1' to alleviate the single and associated effects of salt and drought stress. Moreover, in comparison to DI under salinity, biochar addition in ARD significantly increased plant height, aerial biomass, fruit number per plant, and mean fresh weight per fruit by 18.4%, 39.7%, 37.5%, and 36.3%, respectively. Furthermore, under limited and saline irrigation, photosynthetic rate (A_n), transpiration rate (E), and stomatal conductance (g_s) declined. In addition, the interaction between ARD and biochar effectively restored the equilibrium between the plant chemical signal (ABA) and hydraulic signal (leaf water potential). As a result, mainly under salt stress, with ARD treatment, intrinsic water use efficiency (WUE_i) and yield traits were much higher than those in DI. Overall, biochar in combination with ARD could be an efficient approach for preserving crop productivity.

QTL Analysis Reveals Conserved and Differential Genetic Regulation of Maize Lateral Angles above the Ear

Improving the density tolerance and planting density has great importance for increasing maize production. The key to promoting high density planting is breeding maize with a compact canopy architecture, which is mainly influenced by the angles of the leaves and tassel branches above the ear. It is still unclear whether the leaf angles of different stem nodes and tassel branches are controlled by similar genetic regulatory mechanisms, which limits the ability to breed for density-tolerant maize. Here, we developed a population with 571 double haploid lines derived from inbred lines, PHBA6 and Chang7-2, showing significant differences in canopy architecture. Phenotypic and QTL analyses revealed that the genetic regulation mechanism was largely similar for closely adjacent leaves above the ears. In contrast, the regulation mechanisms specifying the angles of distant leaves and the angles of leaves vs. tassel branches are largely different. The liguless1 gene was identified as a candidate gene for QTLs co-regulating the angles of different leaves and the tassel branch, consistent with its known roles in regulating plant architecture. Our findings can be used to develop strategies for the improvement of leaf and tassel architecture through the introduction of trait-specific or pleiotropic genes, thus benefiting the breeding of maize with increased density tolerance in the future.

Leaf, plant, to canopy: A mechanistic study on aboveground plasticity and plant density within a maize-soybean intercrop system for the Midwest, USA

Plants have evolved to adapt to their neighbours through plastic trait responses. In intercrop systems, plant growth occurs at different spatial and temporal dimensions, creating a competitive light environment where aboveground plasticity may support complementarity in light-use efficiency, realizing yield gains per unit area compared with monoculture systems. Physiological and architectural plasticity including the consequences for light-use efficiency and yield in a maize-soybean solar corridor intercrop system was compared, empirically, with the standard monoculture systems of the Midwest, USA. The impact of reducing maize plant density on yield was investigated in the following year. Intercropped maize favoured physiological plasticity over architectural plasticity, which maintained harvest index (HI) but reduced light interception efficiency (ɛ_i ) and conversion efficiency (ɛ_c ). Intercropped soybean invested in both plasticity responses, which maintained ɛ_i , but HI and ɛ_c decreased. Reducing maize plant density within the solar corridor rows did not improve yields under monoculture and intercrop systems. Overall, the intercrop decreased land-use efficiency by 9%-19% and uncoordinated investment in aboveground plasticity by each crop under high maize plant density does not support complementarity in light-use efficiency. Nonetheless, the mechanistic understanding gained from this study may improve crop cultivars and intercrop designs for the Midwest to increase yield.

Breeding crops for drought-affected environments and improved climate resilience

Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.

Co-expression of stress-responsive regulatory genes, MuNAC4, MuWRKY3 and MuMYB96 associated with resistant-traits improves drought adaptation in transgenic groundnut (Arachis hypogaea l.) plants

Groundnut, cultivated under rain-fed conditions is prone to yield losses due to intermittent drought stress. Drought tolerance is a complex phenomenon and multiple gene expression required to maintain the cellular tolerance. Transcription factors (TFs) regulate many functional genes involved in tolerance mechanisms. In this study, three stress-responsive regulatory TFs cloned from horse gram, (Macrotyloma uniflorum (Lam) Verdc.), MuMYB96, involved in cuticular wax biosynthesis; MuWRKY3, associated with anti-oxidant defense mechanism and MuNAC4, tangled with lateral root development were simultaneously expressed to enhance drought stress resistance in groundnut (Arachis hypogaea L.). The multigene transgenic groundnut lines showed reduced ROS production, membrane damage, and increased superoxide dismutase (SOD) and ascorbate peroxidase (APX) enzyme activity, evidencing improved antioxidative defense mechanisms under drought stress. Multigene transgenic plants showed lower proline content, increased soluble sugars, epicuticular wax content and higher relative water content suggesting higher maintenance of tissue water status compared to wildype and mock plants. The scanning electron microscopy (SEM) analysis showed a substantial increase in deposition of cuticular waxes and variation in stomatal number in multigene transgenic lines compared to wild type and mock plants. The multigene transgenic plants showed increased growth of lateral roots, chlorophyll content, and stay-green nature in drought stress compared to wild type and mock plants. Expression analysis of transgenes, MuMYB96, MuWRKY3, and MuNAC4 and their downstream target genes, KCS6, KCR1, APX3, CSD1, LBD16 and DBP using qRT-PCR showed a two- to four-fold increase in transcript levels in multigene transgenic groundnut plants over wild type and mock plants under drought stress. Our study demonstrate that introducing multiple genes with simultaneous expression of genes is a viable option to improve stress tolerance and productivity under drought stress.

Comparative analysis of farmer practices and high yield experiments: Farmers could get more maize yield from maize-soybean relay intercropping through high density cultivation of maize

Intercropping is a high-yield, resource-efficient planting method. There is a large gap between actual yield and potential yield at farmer's field. Their actual yield of intercropped maize remains unclear under low solar radiation-area, whether this yield can be improved, and if so, what are the underlying mechanism for increasing yield? In the present study, we collected the field management and yield data of intercropping maize by conducting a survey comprising 300 farmer households in 2016-2017. Subsequently, based on surveyed data, we designed an experiment including a high density planting (Dense cultivation and high N fertilization with plough tillage; DC) and normal farmer practice (Common cultivation; CC) to analyze the yield, canopy structure, light interception, photosynthetic parameters, and photosynthetic productivity. Most farmers preferred rotary tillage with a low planting density and N fertilization. Survey data showed that farmer yield ranged between 4-6 Mg ha^-1, with highest yield recorded at 10-12 Mg ha^-1, suggesting a possibility for yield improvement by improved cropping practices. Results from high density experiment showed that the two-years average yield for DC was 28.8% higher than the CC. Compared to CC, the lower angle between stem and leaf (LA) and higher leaf area index (LAI) in DC resulted in higher light interception in middle canopy and increased the photosynthetic productivity under DC. Moreover, in upper and lower canopies, the average activity of phosphoenolpyruvate (PEP) carboxylase was 70% higher in DC than CC. Briefly, increase in LAI and high Pn improved both light interception and photosynthetic productivity, thereby mediating an increase in the maize yield. Overall, these results indicated that farmer's yields on average can be increased by 2.1 Mg ha^-1 by increasing planting density and N fertilization, under plough tillage.

Radiation use efficiency increased over a century of maize (Zea mays L.) breeding in the US corn belt

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.

Modulation of GmFAD3 expression alters abiotic stress responses in soybean

KEY MESSAGE

This study focused on enhancing resilience of soybean crops to drought and salinity stresses by overexpression of GmFAD3A gene, which plays an important role in modulating membrane fluidity and ultimately influence plants response to various abiotic stresses. Fatty acid desaturases (FADs) are a class of enzymes that mediate desaturation of fatty acids by introducing double bonds. They play an important role in modulating membrane fluidity in response to various abiotic stresses. However, a comprehensive analysis of GmFAD3 in drought and salinity stress tolerance in soybean is lacking. We used bean pod mottle virus (BPMV)-based vector for achieving rapid and efficient overexpression as well as silencing of Omega-3 Fatty Acid Desaturase gene from Glycine max (GmFAD3) to assess the functional role of GmFAD3 in abiotic stress responses in soybean. Higher levels of recombinant BPMV-GmFAD3A transcripts were detected in overexpressing soybean plants. Overexpression of GmFAD3A in soybean resulted in increased levels of jasmonic acid and higher expression of GmWRKY54 as compared to mock-inoculated, vector-infected and FAD3-silenced soybean plants under drought and salinity stress conditions. The GmFAD3A-overexpressing plants showed higher levels of chlorophyll content, efficient photosystem-II, relative water content, transpiration rate, stomatal conductance, proline content and also cooler canopy under drought and salinity stress conditions as compared to mock-inoculated, vector-infected and FAD3-silenced soybean plants. Results from the current study revealed that GmFAD3A-overexpressing soybean plants exhibited tolerance to drought and salinity stresses. However, soybean plants silenced for GmFAD3 were vulnerable to drought and salinity stresses.

The field phenotyping platform's next darling: Dicotyledons

The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.

Rhizoboxes as Rapid Tools for the Study of Root Systems of Prunus Seedlings

Rootstocks are fundamental for peach production, and their architectural root traits determine their performance. Root-system architecture (RSA) analysis is one of the key factors involved in rootstock selection. However, there are few RSA studies on Prunus spp., mostly due to the tedious and time-consuming labor of measuring below-ground roots. A root-phenotyping experiment was developed to analyze the RSA of seedlings from ‘Okinawa’ and ‘Guardian’™ peach rootstocks. The seedlings were established in rhizoboxes and their root systems scanned and architecturally analyzed. The root-system depth:width ratio (D:W) throughout the experiment, as well as the root morphological parameters, the depth rooting parameters, and the root angular spread were estimated. The ‘Okinawa’ exhibited greater root morphological traits, as well as the other parameters, confirming the relevance of the spatial disposition and growth pattern of the root system.

Genome-Wide Association Studies of Root-Related Traits in Brassica napus L. under Low-Potassium Conditions

Roots are essential organs for a plant’s ability to absorb water and obtain mineral nutrients, hence they are critical to its development. Plants use root architectural alterations to improve their chances of absorbing nutrients when their supply is low. Nine root traits of a Brassica napus association panel were explored in hydroponic-system studies under low potassium (K) stress to unravel the genetic basis of root growth in rapeseed. The quantitative trait loci (QTL) and candidate genes for root development were discovered using a multilocus genome-wide association study (ML-GWAS). For the nine traits, a total of 453 significant associated single-nucleotide polymorphism (SNP) loci were discovered, which were then integrated into 206 QTL clusters. There were 45 pleiotropic clusters, and qRTA04-4 and qRTC04-7 were linked to TRL, TSA, and TRV at the same time, contributing 5.25–11.48% of the phenotypic variance explained (PVE) to the root traits. Additionally, 1360 annotated genes were discovered by examining genomic regions within 100 kb upstream and downstream of lead SNPs within the 45 loci. Thirty-five genes were identified as possibly regulating root-system development. As per protein–protein interaction analyses, homologs of three genes (BnaC08g29120D, BnaA07g10150D, and BnaC04g45700D) have been shown to influence root growth in earlier investigations. The QTL clusters and candidate genes identified in this work will help us better understand the genetics of root growth traits and could be employed in marker-assisted breeding for rapeseed adaptable to various conditions with low K levels.

Crop improvement influences on water quantity and quality processes in an agricultural watershed

Field crop traits have and are experiencing significant changes due to genetic and agronomic improvements. How these changes affect regional water quantity and quality processes has not been clarified. The St. Joseph River Watershed (SJRW) located in the U.S. Corn Belt was selected as a case study area. Crop (corn and soybean) trait improvements in the past decades were reviewed and summarized and include changes of growing degree days (GDD), leaf area index (LAI), light utilization (LU), drought tolerance (DT), nutrient content (NC), and harvest index (HI). Based on a calibrated 9-year (from 2011 to 2019) SWAT (Soil and Water Assessment Tool) simulation in SJRW, sensitivities of the above crop traits to yield, ET_a, stream flow, tile flow, surface runoff, and nutrient loads (NO₃N, TN, soluble-P, and TP) were analyzed. Crop traits and their corresponding SWAT parameters for the 2010s were obtained from model calibration and used as the baseline/current scenario; for the 1980s, they were summarized from literature review and used as an historical scenario, while those for the 2040s were determined by assuming crop traits are changing linearly with time and projected as the future scenario. Water quantity and quality changes under the historical and future crop scenarios were compared with the baseline/current simulation. Results showed LU and DT were the most sensitive crop traits to water quantity (i.e., ET_a, stream flow, tile flow, and surface runoff), while HI was the most sensitive to nutrient loads. The impacts of crop improvements on nutrient loads were more significant than on water budgets. Compared with the baseline, the historical and future scenarios resulted in 1.5 - 2.0% changes of stream flow, 6.8 - 18.6% changes of nitrogen loads (NO₃N and TN) and 2.6 - 3.9% changes of phosphorus loads (soluble-P, and TP) in the stream flow, annually. Moreover, in certain months, these changes can reach about 12% for stream flow, 42% for nitrogen loads, and 12% for phosphorus loads. Nitrogen losses by tile drainage and percolation, and phosphorus losses by surface runoff and tile drainage were most significantly affected by the crop improvements. Future work should consider expected crop improvements when studying long-term hydrology and nutrient cycles in agricultural watersheds.

Plant Population and Row Spacing Affects Growth and Yield of Rainfed Maize in Semi-arid Environments

Increased tolerance to competition for soil resources of modern maize (Zea mays L.) hybrids increases soil resource use efficiency and yield. Yet little information is available on the relationship between maize population density and yield under no-tillage in semi-arid environments. A 2-year field trial was conducted in South Africa during the 2017/2018 (Season 1) and 2018/2019 (Season 2) production seasons to evaluate growth and water use productivity of rainfed maize established at seven diverse plant population (20,000-60,000 plants ha^-1) and row spacing (0.52 and 0.76 m) configurations. In Season 1, light interception was 6.8% greater at 0.76 m row spacing compared to 0.52 m row spacing (p < 0.05). In Season 2, despite dry and hot growing conditions, a well-developed leaf canopy cover was present at 0.52 m row spacing indicating a 10.4% greater intercepted photosynthetically active radiation (IPAR) compared to 0.76 m row spacing. In Season 1, with more uniform rainfall distribution, no biomass or yield benefits were found with increased plant population, except at 50,000 plants ha^-1 at 0.76 m row spacing. In Season 2, plant populations at 0.76 m row spacing out-yielded any given plant population at 0.52 m row spacing. The optimal plant population and row spacing will ultimately be a compromise between obtaining high maize grain yield and minimizing the potential for crop failure in semi-arid environments.

Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)

Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.

Root system architecture in cereals: progress, challenges and perspective

Roots are essential multifunctional plant organs involved in water and nutrient uptake, metabolite storage, anchorage, mechanical support, and interaction with the soil environment. Understanding of this 'hidden half' provides potential for manipulation of root system architecture (RSA) traits to optimize resource use efficiency and grain yield in cereal crops. Unfortunately, root traits are highly neglected in breeding due to the challenges of phenotyping, but could have large rewards if the variability in RSA traits can be fully exploited. Until now, a plethora of genes have been characterized in detail for their potential role in improving RSA. The use of forward genetics approaches to find sequence variations in genes underpinning desirable RSA would be highly beneficial. Advances in computer vision applications have allowed image-based approaches for high-throughput phenotyping of RSA traits that can be used by any laboratory worldwide to make progress in understanding root function and dissection of the genetics. At the same time, the frontiers of root measurement include non-invasive methods like X-ray computer tomography and magnetic resonance imaging that facilitate new types of temporal studies. Root physiology and ecology are further supported by spatiotemporal root simulation modeling. The discovery of component traits providing improved resilience and yield advantage in target environments is a key necessity for mainstreaming root-based cereal breeding. The integrated use of pan-genome resources, now available in most cereals, coupled with new in-field phenotyping platforms has the potential for precise selection of superior genotypes with improved RSA.

High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings

In plants, the study of belowground traits is gaining momentum due to their importance on yield formation and the uptake of water and nutrients. In several cereal crops, seminal root number and seminal root angle are proxy traits of the root system architecture at the mature stages, which in turn contributes to modulating the uptake of water and nutrients. Along with seminal root number and seminal root angle, experimental evidence indicates that the transpiration rate response to evaporative demand or vapor pressure deficit is a key physiological trait that might be targeted to cope with drought tolerance as the reduction of the water flux to leaves for limiting transpiration rate at high levels of vapor pressure deficit allows to better manage soil moisture. In the present study, we examined the phenotypic diversity of seminal root number, seminal root angle, and transpiration rate at the seedling stage in a panel of 8-way Multiparent Advanced Generation Inter-Crosses lines of winter barley and correlated these traits with grain yield measured in different site-by-season combinations. Second, phenotypic and genotypic data of the Multiparent Advanced Generation Inter-Crosses population were combined to fit and cross-validate different genomic prediction models for these belowground and physiological traits. Genomic prediction models for seminal root number were fitted using threshold and log-normal models, considering these data as ordinal discrete variable and as count data, respectively, while for seminal root angle and transpiration rate, genomic prediction was implemented using models based on extended genomic best linear unbiased predictors. The results presented in this study show that genome-enabled prediction models of seminal root number, seminal root angle, and transpiration rate data have high predictive ability and that the best models investigated in the present study include first-order additive × additive epistatic interaction effects. Our analyses indicate that beyond grain yield, genomic prediction models might be used to predict belowground and physiological traits and pave the way to practical applications for barley improvement.

Breeding effects on canopy light attenuation in maize: a retrospective and prospective analysis

The light attenuation process within a plant canopy defines energy capture and vertical distribution of light and nitrogen (N). The vertical light distribution can be quantitatively described with the extinction coefficient (k), which associates the fraction of intercepted photosynthetically active radiation (fPARi) with the leaf area index (LAI). Lower values of k correspond to upright leaves and homogeneous vertical light distribution, increasing radiation use efficiency (RUE). Yield gains in maize (Zea mays L.) were accompanied by increases in optimum plant density and leaf erectness. Thus, the yield-driven breeding programs and management changes, such as reduced row spacing, selected a more erect leaf habit under different maize production systems (e.g., China and the USA). In this study, data from Argentina revealed that k decreased at a rate of 1.1% year-1 since 1989, regardless of plant density and in agreement with Chinese reports (1.0% year-1 since 1981). A reliable assessment of changes in k over time is critical for predicting (i) modifications in resource use efficiency (e.g. radiation, water, and N), improving estimations derived from crop simulation models; (ii) differences in productivity caused by management practices; and (iii) limitations to further exploit this trait with breeding.

Root angle in maize influences nitrogen capture and is regulated by calcineurin B-like protein (CBL)-interacting serine/threonine-protein kinase 15 (ZmCIPK15)

Crops with reduced nutrient and water requirements are urgently needed in global agriculture. Root growth angle plays an important role in nutrient and water acquisition. A maize diversity panel of 481 genotypes was screened for variation in root angle employing a high-throughput field phenotyping platform. Genome-wide association mapping identified several single nucleotide polymorphisms (SNPs) associated with root angle, including one located in the root expressed CBL-interacting serine/threonine-protein kinase 15 (ZmCIPK15) gene (LOC100285495). Reverse genetic studies validated the functional importance of ZmCIPK15, causing a approximately 10° change in root angle in specific nodal positions. A steeper root growth angle improved nitrogen capture in silico and in the field. OpenSimRoot simulations predicted at 40 days of growth that this change in angle would improve nitrogen uptake by 11% and plant biomass by 4% in low nitrogen conditions. In field studies under suboptimal N availability, the cipk15 mutant with steeper growth angles had 18% greater shoot biomass and 29% greater shoot nitrogen accumulation compared to the wild type after 70 days of growth. We propose that a steeper root growth angle modulated by ZmCIPK15 will facilitate efforts to develop new crop varieties with optimal root architecture for improved performance under edaphic stress.

Missense mutation of a class B heat shock factor is responsible for the tomato bushy root-2 phenotype

The bushy root-2 (brt-2) tomato mutant has twisting roots, and slower plant development. Here we used whole genome resequencing and genetic mapping to show that brt-2 is caused by a serine to cysteine (S75C) substitution in the DNA binding domain (DBD) of a heat shock factor class B (HsfB) encoded by SolycHsfB4a. This gene is orthologous to the Arabidopsis SCHIZORIZA gene, also known as AtHsfB4. The brt-2 phenotype is very similar to Arabidopsis lines in which the function of AtHsfB4 is altered: a proliferation of lateral root cap and root meristematic tissues, and a tendency for lateral root cap cells to easily separate. The brt-2 S75C mutation is unusual because all other reported amino acid substitutions in the highly conserved DBD of eukaryotic heat shock factors are dominant negative mutations, but brt-2 is recessive. We further show through reciprocal grafting that brt-2 exerts its effects predominantly through the root genotype even through BRT-2 is expressed at similar levels in both root and shoot meristems. Since AtHsfB4 is induced by root knot nematodes (RKN), and loss-of-function mutants of this gene are resistant to RKNs, BRT-2 could be a target gene for RKN resistance, an important trait in tomato rootstock breeding.Gene & accession numbersSolycHsfB4a - Solyc04g078770.

Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding?

Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m-2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM-WGP) demonstrated that CGM-WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.

Genetic control of leaf angle in sorghum and its effect on light interception

Developing sorghum genotypes adapted to different light environments requires understanding of a plant's ability to capture light, determined through leaf angle specifically. This study dissected the genetic basis of leaf angle in 3 year field trials at two sites, using a sorghum diversity panel (729 accessions). A wide range of variation in leaf angle with medium heritability was observed. Leaf angle explained 36% variation in canopy light extinction coefficient, highlighting the extent to which variation in leaf angle influences light interception at the whole-canopy level. This study also found that the sorghum races of Guinea and Durra consistently having the largest and smallest leaf angle, respectively, highlighting the potential role of leaf angle in adaptation to distinct environments. The genome-wide association study detected 33 quantitative trait loci (QTLs) associated with leaf angle. Strong synteny was observed with previously detected leaf angle QTLs in maize (70%) and rice (40%) within 10 cM, among which the overlap was significantly enriched according to χ2 tests, suggesting a highly consistent genetic control in grasses. A priori leaf angle candidate genes identified in maize and rice were found to be enriched within a 1-cM window around the sorghum leaf angle QTLs. Additionally, protein domain analysis identified the WD40 protein domain as being enriched within a 1-cM window around the QTLs. These outcomes show that there is sufficient heritability and natural variation in the angle of upper leaves in sorghum which may be exploited to change light interception and optimize crop canopies for different contexts.

Functional-Structural Plant Models Mission in Advancing Crop Science: Opportunities and Prospects

Functional-structural plant models (FSPMs) have been evolving for over 2 decades and their future development, to some extent, depends on the value of potential applications in crop science. To date, stabilizing crop production by identifying valuable traits for novel cultivars adapted to adverse environments is topical in crop science. Thus, this study will examine how FSPMs are able to address new challenges in crop science for sustainable crop production. FSPMs developed to simulate organogenesis, morphogenesis, and physiological activities under various environments and are amenable to downscale to the tissue, cellular, and molecular level or upscale to the whole plant and ecological level. In a modeling framework with independent and interactive modules, advanced algorithms provide morphophysiological details at various scales. FSPMs are shown to be able to: (i) provide crop ideotypes efficiently for optimizing the resource distribution and use for greater productivity and less disease risk, (ii) guide molecular design breeding via linking molecular basis to plant phenotypes as well as enrich crop models with an additional architectural dimension to assist breeding, and (iii) interact with plant phenotyping for molecular breeding in embracing three-dimensional (3D) architectural traits. This study illustrates that FSPMs have great prospects in speeding up precision breeding for specific environments due to the capacity for guiding and integrating ideotypes, phenotyping, molecular design, and linking molecular basis to target phenotypes. Consequently, the promising great applications of FSPMs in crop science will, in turn, accelerate their evolution and vice versa.

Managing Density Stress to Close the Maize Yield Gap

Continued yield increases of maize (Zea mays L.) will require higher planting populations, and enhancement of other agronomic inputs could alleviate density-induced stress. Row spacing, plant population, P-S-Zn fertility, K-B fertility, N fertility, and foliar protection were evaluated for their individual and cumulative impacts on the productivity of maize in a maize-soybean [Glycine max (L.) Merr.] rotation. An incomplete factorial design with these agronomic factors in both 0.76 and 0.51 m row widths was implemented for 13 trials in Illinois, United States, from 2014 to 2018. The agronomic treatments were compared to two controls: enhanced and standard, comprising all the factors applied at the enhanced or standard level, respectively. The 0.51 m enhanced management control yielded 3.3 Mg ha^-1 (1.8-4.6 Mg ha^-1 across the environments) more grain (25%) than the 0.76 m standard management control, demonstrating the apparent yield gap between traditional farm practices and attainable yield through enhanced agronomic management. Narrow rows and the combination of P-S-Zn and K-B fertility were the factors that provided the most significant yield increases over the standard control. Increasing plant population from 79,000 to 109,000 plants ha^-1 reduced the yield gap when all other inputs were applied at the enhanced level. However, increasing plant population alone did not increase yield when no other factors were enhanced. Some agronomic factors, such as narrow rows and availability of plant nutrition, become more critical with increasing plant population when density-induced stress is more significant. Changes in yield were dependent upon changes in kernel number. Kernel weight was the heaviest when all the management factors were applied at the enhanced level while only planting 79,000 plants ha^-1. Conversely, kernel weight was the lightest when increasing population to 109,000 plants ha^-1 while all other factors were applied at the standard level. The yield contribution of each factor was generally greater when applied in combination with all other enhanced factors than when added individually to the standard input system. Additionally, the full value of high-input agronomic management was only realized when matched with greater plant density.

Phenotyping seedlings for selection of root system architecture in alfalfa (Medicago sativa L.)

BACKGROUND

The root system architecture (RSA) of alfalfa (Medicago sativa L.) affects biomass production by influencing water and nutrient uptake, including nitrogen fixation. Further, roots are important for storing carbohydrates that are needed for regrowth in spring and after each harvest. Previous selection for a greater number of branched and fibrous roots significantly increased alfalfa biomass yield. However, phenotyping root systems of mature alfalfa plant is labor-intensive, time-consuming, and subject to environmental variability and human error. High-throughput and detailed phenotyping methods are needed to accelerate the development of alfalfa germplasm with distinct RSAs adapted to specific environmental conditions and for enhancing productivity in elite germplasm. In this study methods were developed for phenotyping 14-day-old alfalfa seedlings to identify measurable root traits that are highly heritable and can differentiate plants with either a branched or a tap rooted phenotype. Plants were grown in a soil-free mixture under controlled conditions, then the root systems were imaged with a flatbed scanner and measured using WinRhizo software.

RESULTS

The branched root plants had a significantly greater number of tertiary roots and significantly longer tertiary roots relative to the tap rooted plants. Additionally, the branch rooted population had significantly more secondary roots > 2.5 cm relative to the tap rooted population. These two parameters distinguishing phenotypes were confirmed using two machine learning algorithms, Random Forest and Gradient Boosting Machines. Plants selected as seedlings for the branch rooted or tap rooted phenotypes were used in crossing blocks that resulted in a genetic gain of 10%, consistent with the previous selection strategy that utilized manual root scoring to phenotype 22-week-old-plants. Heritability analysis of various root architecture parameters from selected seedlings showed tertiary root length and number are highly heritable with values of 0.74 and 0.79, respectively.

CONCLUSIONS

The results show that seedling root phenotyping is a reliable tool that can be used for alfalfa germplasm selection and breeding. Phenotypic selection of RSA in seedlings reduced time for selection by 20 weeks, significantly accelerating the breeding cycle.

Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

In this study, we evaluated the suitability of semi-arid region of Central Morocco for wheat production using Agricultural Production Systems sIMulator (APSIM) considering weather, soil properties and crop management production factors. Model calibration was carried out using data collected from field trials. A quantitative statistics, i.e., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and index of agreement (d) were used in model performance evaluation. Furthermore, series of simulations were performed to simulate the future scenarios of wheat productivity based on climate projection; the optimum sowing date under water deficit condition and selection of appropriate wheat varieties. The study showed that the performance of the model was fairly accurate as judged by having RMSE = 0.13, NSE = 0.95, and d = 0.98. The realization of future climate data projection and their integration into the APSIM model allowed us to obtain future scenarios of wheat yield that vary between 0 and 2.33 t/ha throughout the study period. The simulated result confirmed that the yield obtained from plots seeded between 25 October and 25 November was higher than that of sown until 05 January. From the several varieties tested, Hartog, Sunstate, Wollaroi, Batten and Sapphire were yielded comparatively higher than the locale variety Marzak. In conclusion, APSIM-Wheat model could be used as a promising tool to identify the best management practices such as determining the sowing date and selection of crop variety based on the length of the crop cycle for adapting and mitigating climate change.

Optimum Planting Density Improves Resource Use Efficiency and Yield Stability of Rainfed Maize in Semiarid Climate

Increasing planting density is an effective strategy for improving maize productivity, but grain yield does not increase linearly with the increase in plant density, especially in semiarid environments. However, how planting density regulates the integrated utilization of key input resources (i.e., radiation, water, and nutrients) to affect maize production is not clear. To evaluate the effects of planting density and cultivar on maize canopy structure, photosynthetic characteristics, yield, and resource use efficiency, we conducted a successive field experiment from 2013 to 2018 in Heyang County (Shaanxi Province, China) using three different cultivars [i.e., Yuyu22 (C1), Zhengdan958 (C2), and Xianyu335 (C3)] at four planting densities [i.e., 52,500 (D1), 67,500 (D2), 82,500 (D3), and 97,500 (D4) plants ha^-1]. Increasing planting density significantly increased the leaf area index (LAI) and the amount of intercepted photosynthetically active radiation (IPAR), thereby promoting plant growth and crop productivity. However, increased planting density reduced plant photosynthetic capacity [net photosynthetic rate (Pn)], stomatal conductance (Gc), and leaf chlorophyll content. These alterations constitute key mechanisms underlying the decline in crop productivity and yield stability at high planting density. Although improved planting density increased IPAR, it did not promote higher resource use efficiency. Compared with the D1 treatment, the grain yield, precipitation use efficiency (PUE), radiation use efficiency (RUE), and nitrogen use efficiency (NUE) increased by 5.6-12.5%, 2.8-7.1%, and -2.1 to 1.6% in D2, D3, and D4 treatments, respectively. These showed that pursuing too high planting density is not a desirable strategy in the rainfed farming system of semiarid environments. In addition, density-tolerant cultivars (C2 and C3) showed better canopy structure and photosynthetic capacity and recorded higher yield stability and resource use efficiency. Together, these results suggest that growing density-tolerant cultivars at moderate planting density could serve as a promising approach for stabilizing grain yield and realizing the sustainable development of agriculture in semiarid regions.

Developing Functional Relationships between Soil Waterlogging and Corn Shoot and Root Growth and Development

Short- and long-term waterlogging conditions impact crop growth and development, preventing crops from reaching their true genetic potential. Two experiments were conducted using a pot-culture facility to better understand soil waterlogging impacts on corn growth and development. Two corn hybrids were grown in 2017 and 2018 under ambient sunlight and temperature conditions. Waterlogging durations of 0, 2, 4, 6, 8, 10, 12, and 14 days were imposed at the V2 growth stage. Morphological (growth and development) and pigment estimation data were collected 15 days after treatments were imposed, 23 days after sowing. As waterlogging was imposed, soil oxygen rapidly decreased until reaching zero in about 8-10 days; upon the termination of the treatments, the oxygen levels recovered to the level of the 0 days treatment within 2 days. Whole-plant dry weight declined as the waterlogging duration increased, and after 2 days of waterlogging, a 44% and 27% decline was observed in experiments 1 and 2, respectively. Leaf area and root volume showed an exponential decay similar to the leaf and root dry weight. Leaf number and plant height were the least sensitive measured parameters and decreased linearly in both experiments. Root forks were the most sensitive parameter after 14 days of waterlogging in both experiments, declining by 83% and 80% in experiments 1 and 2, respectively. The data from this study improve our understanding of how corn plants react to increasing durations of waterlogging. In addition, the functional relationships generated from this study could enhance current corn simulation models for field applications.

Canopy occupation volume as an indicator of canopy photosynthetic capacity

Leaf angle and leaf area index together influence canopy light interception and canopy photosynthesis. However, so far, there is no effective method to identify the optimal combination of these two parameters for canopy photosynthesis. In this study, first a robust high-throughput method for accurate segmentation of maize organs based on 3D point clouds data was developed, then the segmented plant organs were used to generate new 3D point clouds for the canopy of altered architectures. With this, we simulated the synergistic effect of leaf area and leaf angle on canopy photosynthesis. The results show that, compared to the traditional parameters describing the canopy photosynthesis including leaf area index, facet angle and canopy coverage, a new parameter - the canopy occupation volume (COV) - can better explain the variations of canopy photosynthetic capacity. Specifically, COV can explain > 79% variations of canopy photosynthesis generated by changing leaf angle and > 84% variations of canopy photosynthesis generated by changing leaf area. As COV can be calculated in a high-throughput manner based on the canopy point clouds, it can be used to evaluate canopy architecture in breeding and agronomic research.

Drought imprints on crops can reduce yield loss: Nature's insights for food security

The Midwestern “Corn‐Belt” in the United States is the most productive agricultural region on the planet despite being predominantly rainfed. In this region, global climate change is driving precipitation patterns toward wetter springs and drier mid‐ to late‐summers, a trend that is likely to intensify in the future. The lack of precipitation can lead to crop water limitations that ultimately impact growth and yields. Young plants exposed to water stress will often invest more resources into their root systems, possibly priming the crop for any subsequent mid‐ or late‐season drought. The trend toward wetter springs, however, suggests that opportunities for crop priming may lessen in the future. Here, we test the hypothesis that early season dry conditions lead to drought priming in field‐grown crops and this response will protect crops against growth and yield losses from late‐season droughts. This hypothesis was tested for the two major Midwestern crop, maize and soybean, using high‐resolution daily weather data, satellite‐derived phenological metrics, field yield data, and ecosystem‐scale model (Agricultural Production System Simulator) simulations. The results from this study showed that priming mitigated yield losses from a late season drought of up to 4.0% and 7.0% for maize and soybean compared with unprimed crops experiencing a late season drought. These results suggest that if the trend toward wet springs with drier summers continues, the relative impact of droughts on crop productivity is likely to worsen. Alternatively, identifying opportunities to breed or genetically modify pre‐primed crop species may provide improved resilience to future climate change.