Simultaneous gains in grain yield and nitrogen efficiency over 70 years of maize genetic improvement

Genetic trends in CIMMYT's tropical maize breeding pipelines

Fostering a culture of continuous improvement through regular monitoring of genetic trends in breeding pipelines is essential to improve efficiency and increase accountability. This is the first global study to estimate genetic trends across the International Maize and Wheat Improvement Center (CIMMYT) tropical maize breeding pipelines in eastern and southern Africa (ESA), South Asia, and Latin America over the past decade. Data from a total of 4152 advanced breeding trials and 34,813 entries, conducted at 1331 locations in 28 countries globally, were used for this study. Genetic trends for grain yield reached up to 138 kg ha^-1 yr^-1 in ESA, 118 kg ha^-1 yr^-1 South Asia and 143 kg ha^-1 yr^-1 in Latin America. Genetic trend was, in part, related to the extent of deployment of new breeding tools in each pipeline, strength of an extensive phenotyping network, and funding stability. Over the past decade, CIMMYT's breeding pipelines have significantly evolved, incorporating new tools/technologies to increase selection accuracy and intensity, while reducing cycle time. The first pipeline, Eastern Africa Product Profile 1a (EA-PP1a), to implement marker-assisted forward-breeding for resistance to key diseases, coupled with rapid-cycle genomic selection for drought, recorded a genetic trend of 2.46% per year highlighting the potential for deploying new tools/technologies to increase genetic gain.

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.

Energy policy and coastal water quality: An integrated energy, air and water quality modeling approach

Federal policy changes in the management of carbon emissions from power plants offer a potent real-world example for examining air-land-water interactions and their implications for coastal water quality. We integrate models of energy (Integrated Planning Model (IPM)), air quality (Community Multiscale Air Quality (CMAQ) and water quality (SPAtially Referenced Regression On Watershed attributes (SPARROW)) to investigate the potential water quality impacts of policy-driven changes in total nitrogen deposition in watersheds draining to US coastal areas. We estimate the combined effects of three recently proposed energy policy scenarios, population growth, and climate change. We decompose the combined effects into the roles of the individual components on the supply of riverine nitrogen for the entire US and eight coastal regions. We find that population growth is the most important driver of changes in coastal nitrogen flux. Energy policies play a minor role in offsetting the negative effects of population growth, although the effect varies by energy policy and region. The greatest population and policy effects are projected for the Gulf of Mexico. Given limited reductions in nitrogen emissions and deposition associated with energy policies, the net effect of policy and population changes is an increase in total nitrogen flux to all estuaries relative to the 2010 baseline. While population growth increases flux, and energy policies decrease flux in all regions, climate change can either increase or decrease flux depending on the region. That is because the relatively large individual effects of temperature and precipitation on watershed nitrogen processes work in opposing directions. The net result of the offsetting nature of individual climate processes varies in both magnitude and direction by coastal region. Further research is needed to sort out individual temperature and precipitation effects in different regions.

Hyperspectral Indices for Predicting Nitrogen Use Efficiency in Maize Hybrids

Enhancing the nitrogen (N) efficiency of maize hybrids is a common goal of researchers, but involves repeated field and laboratory measurements that are laborious and costly. Hyperspectral remote sensing has recently been investigated for measuring and predicting biomass, N content, and grain yield in maize. We hypothesized that vegetation indices (HSI) obtained mid-season through hyperspectral remote sensing could predict whole-plant biomass per unit of N taken up by plants (i.e., N conversion efficiency: NCE) and grain yield per unit of plant N (i.e., N internal efficiency: NIE). Our objectives were to identify the best mid-season HSI for predicting end-of-season NCE and NIE, rank hybrids by the selected HSI, and evaluate the effect of decreased spatial resolution on the HSI values and hybrid rankings. Analysis of 20 hyperspectral indices from imaging at V16/18 and R1/R2 by manned aircraft and UAVs over three site-years using mixed models showed that two indices, HBSI1 and HBS2, were predictive of NCE, and two indices, HBCI8 and HBCI9, were predictive of NIE for actual data collected from five to nine hybrids at maturity. Statistical differentiation of hybrids in their NCE or NIE performance was possible based on the models with the greatest accuracy obtained for NIE. Lastly, decreasing the spatial resolution changed the HSI values, but an effect on hybrid differentiation was not evident.

Greenhouse gas emissions and mitigation potential of hybrid maize seed production in northwestern China

Although hybrid maize seed production is one of the most important agriculture systems worldwide, its greenhouse gas (GHG) emissions and potential mitigation measures have not been studied. In this study, we used life cycle assessment (LCA) to quantify the GHG emissions of 150 farmers run by 6 companies in an area of northwest China known for hybrid maize seed production. The results indicated that the average reactive nitrogen (Nr) losses and GHG emissions from hybrid maize seed production were 53 kg N ha^-1 and 8077 kg CO₂ eq ha^-1, respectively. Furthermore, the average nitrogen and carbon footprints of the process were 12.2 kg N Mg^-1 and 1495 kg CO₂ eq Mg^-1, respectively. Nitrogen fertilizer and electricity consumption for irrigation were the main contributors to high GHG emissions, accounting for 60% and 30% of the total, respectively. The GHG emissions from seed production for different companies varied greatly with their resource input. There was also a large variation in environmental burdens among the 150 farmers. Based on an analysis of the yield group, we found that the carbon footprint of the first group (the one with the highest yield) was 27% lower than the overall average. Scenario analysis suggests that a combined reduction of N input rate, optimizing irrigation, and increasing yield can eventually mitigate the carbon footprint of hybrid maize seed production by 37%. An integrated systematic approach (e.g., ISSM: integrated soil-crop system management) can reduce the GHG emissions involved in producing hybrid maize seeds. This study provides quantitative evidence and a potential strategy for GHG emissions reduction of hybrid maize seed production.

Matching of Nitrogen Enhancement and Photosynthetic Efficiency by Arbuscular Mycorrhiza in Maize (Zea mays L.) in Relation to Organic Fertilizer Type

In the present study, Funneliformis mosseae (FM), Claroideoglomus etunicatum (CE), and Acaulospora foveata (AF) were inoculated to hybrid maize (Zea mays L. cv. CP888®). Upregulation of nitrogen levels were dependent on the type of mycorrhiza (AMF). Photosynthetic efficiency (Fv/Fm) and water content in FM- and AF-inoculated plants were elevated, resulting in promotion of leaf area and shoot biomass. N content in the shoot and root tissues of the FM-inoculated plants increased by 21% and 30% over the control. A positive correlation between biochemical, physiological, and morphological parameters using Pearson’s coefficient was demonstrated. A decline in lipid peroxidation was noticed in the FM-inoculated plants. In addition, we investigated the potential of N fertilizer application in combination with FM inoculation in maize plants. The FM-inoculated plants with organic O_LT, a chicken manure fertilizer, increased N content in the host shoots by 73% over the control, leading to improved Fv/Fm as a physiological adaptation strategy. The FM and the O_LT on the regulation of the N enhancement and photosynthetic efficiency of the hybrid maize should further be validated in field trials in different environments for sustainability.

Post-silking 15N labelling reveals an enhanced nitrogen allocation to leaves in modern maize (Zea mays) genotypes

Nitrogen (N) metabolism is a major research target for increasing productivity in crop plants. In maize (Zea mays L.), yield gain over the last few decades has been associated with increased N absorption and utilization efficiency (i.e. grain biomass per unit of N absorbed). However, a dynamical framework is still needed to unravel the role of internal processes such as uptake, allocation, and translocation of N in these adaptations. This study aimed to 1) characterize how genetic enhancement in N efficiency conceals changes in allocation and translocation of N, and 2) quantify internal fluxes behind grain N sources in two historical genotypes under high and low N supply. The genotypes 3394 and P1197, landmark hybrids representing key eras of genetic improvement (1990s and 2010s), were grown under high and low N supply in a two-year field study. Using stable isotope ¹⁵N labelling, post-silking nitrogen fluxes were modeled through Bayesian estimation by considering the external N (exogenous-N) and the pre-existing N (endogenous-N) supply across plant organs. Regardless of N availability, P1197 exhibited greater exogenous-N accumulated in leaves and cob-husks. This response was translated to a larger amount of N mobilized to grains (as endogenous-N) during grain-filling in this genotype. Furthermore, the enhanced N supply to leaves in P1197 was associated with increased post-silking carbon accumulation. The overall findings suggest that increased N utilization efficiency over time in maize genotypes was associated with an increased allocation of N to leaves and subsequent translocation to the grains.

Maize-Brachiaria intercropping: A strategy to supply recycled N to maize and reduce soil N2O emissions?

Nitrogen use in agriculture directly impacts food security, global warming, and environmental degradation. Forage grasses intercropped with maize produce feed for animals and or mulch for no-till systems. Forage grasses may exude nitrification inhibitors. It was hypothesized that brachiaria intercropping increases N recycling and maize grain yield and reduces nitrous oxide (N₂O) emissions from soil under maize cropping. A field experiment was set up in December 2016 to test three cropping system (maize monocropped, maize intercropped with Brachiaria brizantha or with B. humidicola) and two N rates (0 or 150 kg ha^-1). The grasses were sown with maize, but B. humidicola did not germinate well in the first year. B. brizantha developed slowly during the maize cycle because of shading but expanded after maize was harvested. The experiment was repeated in 2017/2018 when B. humidicola was replanted. N₂O and carbon dioxide (CO₂) emissions, maize grain yield and N content were measured during the two seasons. After the first maize harvest, the above- and below-ground biomass, C and N content of B. brizantha grown during fall-winter, and the biological nitrification inhibition potential of B. brizantha were evaluated. Maize yield responded to N fertilization (5.1 vs. 9.8 t ha^-1) but not to brachiaria intercropping. B. brizantha recycled approximately 140 kg N ha^-1 and left 12 t dry matter ha^-1 for the second maize crop. However, the 2017/18 maize yields were not affected by the N recycled by B. brizantha, whereas N₂O emissions were higher in the plots with brachiaria, suggesting that part of the recycled N was released too early after desiccation. Brachiarias showed no evidence of causing nitrification inhibition. The strategy of intercropping brachiarias did not increase maize yield, although it added C and recycled N in the system.

Increased seminal root number associated with domestication improves nitrogen and phosphorus acquisition in maize seedlings

BACKGROUND AND AIMS

Domesticated maize (Zea mays ssp. mays) generally forms between two and six seminal roots, while its wild ancestor, Mexican annual teosinte (Zea mays ssp. parviglumis), typically lacks seminal roots. Maize also produces larger seeds than teosinte, and it generally has higher growth rates as a seedling. Maize was originally domesticated in the tropical soils of southern Mexico, but it was later brought to the Mexican highlands before spreading to other parts of the continent, where it experienced different soil resource constraints. The aims of this study were to understand the impacts of increased seminal root number on seedling nitrogen and phosphorus acquisition and to model how differences in maize and teosinte phenotypes might have contributed to increased seminal root number in domesticated maize.

METHODS

Seedling root system architectural models of a teosinte accession and a maize landrace were constructed by parameterizing the functional-structural plant model OpenSimRoot using plants grown in mesocosms. Seedling growth was simulated in a low-phosphorus environment, multiple low-nitrogen environments, and at variable planting densities. Models were also constructed to combine individual components of the maize and teosinte phenotypes.

KEY RESULTS

Seminal roots contributed ~35 % of the nitrogen and phosphorus acquired by maize landrace seedlings in the first 25 d after planting. Increased seminal root number improved plant nitrogen acquisition under low-nitrogen environments with varying precipitation patterns, fertilization rates, soil textures and planting densities. Models suggested that the optimal number of seminal roots for nutrient acquisition in teosinte is constrained by its limited seed carbohydrate reserves.

CONCLUSIONS

Seminal roots can improve the acquisition of both nitrogen and phosphorus in maize seedlings, and the increase in seed size associated with maize domestication may have facilitated increased seminal root number.

Dry Matter Gains in Maize Kernels Are Dependent on Their Nitrogen Accumulation Rates and Duration during Grain Filling

Progressive N assimilation by maize kernels may constrain dry matter (DM) accumulation and final kernel weights (KW). We sought to better understand whole-plant and kernel N mechanisms associated with incremental DM and N accumulation patterns in kernels during grain fill. Maize was grown with multiple fertilizer N rates and N timings or plant densities to achieve a wide N availability gradient. Whole-plant DM and N sampling enabled determination of apparent N nutrition sufficiency at flowering (NNI_R1) and when linear-fill began (NNI_R3). Linear-plateau, mixed-effects models were fitted to kernel DM and N accumulation data collected weekly from early R3. Higher N supply, regardless of application timing or plant density, increased grain-fill duration (GFD) and, more inconsistently, effective grain-filling rate (EGFR). Kernels accumulated DM and N for similar durations. Both final KW and kernel N content increased consistently with N availability mostly because of higher kernel N accumulation rates (KNAR) and duration (KNAD). Both NNI_R1 and NNI_R3 were positively associated with KNAD and KNAR, and less strongly with EGFR. These results confirm the direct role of kernel N accumulation, in addition to prior NNI, in limiting KW gain rates and duration during grain filling.

Lengthening of maize maturity time is not a widespread climate change adaptation strategy in the US Midwest

Increasing temperatures in the US Midwest are projected to reduce maize yields because warmer temperatures hasten reproductive development and, as a result, shorten the grain fill period. However, there is widespread expectation that farmers will mitigate projected yield losses by planting longer season hybrids that lengthen the grain fill period. Here, we ask: (a) how current hybrid maturity length relates to thermal availability of the local climate, and (b) if farmers are shifting to longer season hybrids in response to a warming climate. To address these questions, we used county-level Pioneer brand hybrid sales (Corteva Agriscience) across 17 years and 650 counties in 10 Midwest states (IA, IL, IN, MI, MN, MO, ND, OH, SD, and WI). Northern counties were shown to select hybrid maturities with growing degree day (GDD°C) requirements more closely related to the environmentally available GDD compared to central and southern counties. This measure, termed "thermal overlap," ranged from complete 106% in northern counties to a mere 63% in southern counties. The relationship between thermal overlap and latitude was fit using split-line regression and a breakpoint of 42.8°N was identified. Over the 17-years, hybrid maturities shortened across the majority of the Midwest with only a minority of counties lengthening in select northern and southern areas. The annual change in maturity ranged from -5.4 to 4.1 GDD year^-1 with a median of -0.9 GDD year^-1 . The shortening of hybrid maturity contrasts with widespread expectations of hybrid maturity aligning with magnitude of warming. Factors other than thermal availability appear to more strongly impact farmer decision-making such as the benefit of shorter maturity hybrids on grain drying costs, direct delivery to ethanol biorefineries, field operability, labor constraints, and crop genetics availability. Prediction of hybrid choice under future climate scenarios must include climatic factors, physiological-genetic attributes, socio-economic, and operational constraints.

Reduction in global habitat loss from fossil-fuel-dependent increases in cropland productivity

Terrestrial biodiversity loss and climate change, driven mainly by loss of habitat to agriculture and fossil fuel (FF) use, respectively, are considered among the world's greatest environmental threats. However, FF-dependent technologies are currently essential for manufacturing synthetic nitrogen fertilizers (SNFs) and synthetic pesticides (SPs) critical to increasing agricultural productivity, which reduces habitat loss. Fossil fuel use increases CO₂ levels, further enhancing agricultural productivity. Based on estimates of global increases in yields from SNFs, SPs, and atmospheric CO₂ fertilization, I estimated that FF-dependent technologies are responsible for at least 62.5% of current global food production (GFP) from cropland. Thus, if FF use is eschewed in the future, maintaining current GFP means croplands would have to increase from 12.2% of global land area (GLA) excluding Antarctica to 32.7%. The additional 20.4% of GLA needed exceeds habitat lost currently to cropland (12.2% of GLA) and cumulative conservation areas globally (14.6% of GLA). Thus, although eliminating FF use could reduce climate change, its unintended consequences may be to significantly exacerbate biodiversity loss and indirectly increase food costs, reducing food security which, moreover, disproportionately affects the poor. Although it may be possible to replace SNFs and SPs with FF-free technologies, such substitutes have not yet been demonstrated to be sufficiently economical or efficient. In the interim, meeting global food demand and keeping food prices affordable would increase habitat conversion and food prices. These trade-offs should be considered in analyses of climate change policies.

A time-dependent parameter estimation framework for crop modeling

The performance of crop models in simulating various aspects of the cropping system is sensitive to parameter calibration. Parameter estimation is challenging, especially for time-dependent parameters such as cultivar parameters with 2-3 years of lifespan. Manual calibration of the parameters is time-consuming, requires expertise, and is prone to error. This research develops a new automated framework to estimate time-dependent parameters for crop models using a parallel Bayesian optimization algorithm. This approach integrates the power of optimization and machine learning with prior agronomic knowledge. To test the proposed time-dependent parameter estimation method, we simulated historical yield increase (from 1985 to 2018) in 25 environments in the US Corn Belt with APSIM. Then we compared yield simulation results and nine parameter estimates from our proposed parallel Bayesian framework, with Bayesian optimization and manual calibration. Results indicated that parameters calibrated using the proposed framework achieved an 11.6% reduction in the prediction error over Bayesian optimization and a 52.1% reduction over manual calibration. We also trained nine machine learning models for yield prediction and found that none of them was able to outperform the proposed method in terms of root mean square error and R2. The most significant contribution of the new automated framework for time-dependent parameter estimation is its capability to find close-to-optimal parameters for the crop model. The proposed approach also produced explainable insight into cultivar traits' trends over 34 years (1985-2018).

Can We Harness "Enviromics" to Accelerate Crop Improvement by Integrating Breeding and Agronomy?

The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of "enviromics" as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate "enviromics" methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.

Genetic Progress of Seed Yield and Nitrogen Use Efficiency of Brazilian carioca Common Bean Cultivars Using Bayesian Approaches

Common bean (Phaseolus vulgaris L.) is one of the most important crops worldwide and is considered an essential source of proteins, fibers, and minerals in the daily diet of several countries. Nitrogen (N) is considered the most important nutrient for common bean crop. On the other hand, the reduction of chemical fertilizers is a global challenge, and the development of cultivars with more N use efficiency (NUsE) is considered one of the main strategies to reduce the amount of N fertilizers. Genetic progress of NUsE has been reported in several crops; however, there was still no quantity in common bean. In this study, our goal was to analyze the genetic progress of seed yield (SY) and NUsE-related traits of 40 carioca common bean cultivars release from 1970 to 2017 in eight environments under low (zero) or high N (40 kg ha-1) in top-dressing. Genetic progress, principal component analysis, correlations among traits, and cultivar stability were analyzed using Bayesian approaches. The lowest values of the deviance information criterion (DIC) for the full model tested indicated the presence of the genotype × N × environment interaction for all evaluated traits. Nitrogen utilization efficiency (NUtE) and nitrogen uptake efficiency (NUpE) were the traits that most contributed to discriminate cultivars. The genetic progress of SY under high N (0.53% year-1, 95% HPD = 0.39; 0.65% year-1) was similar to that obtained in low N conditions (0.48% year-1, 95% HPD = 0.31; 0.64% year-1). These results indicate that modern cultivars do not demand more N fertilizers to be more productive. In addition, we observed a high genetic variability for NUsE-related traits, but there was no genetic progress for these variables. SY showed negative correlation with seed protein content (Prot) in both N conditions, and there was no reduction in Prot in modern cultivars. Both modern and old cultivars showed adaptability and stability under contrasting N conditions. Our study contributed to improve our knowledge about the genetic progress of common bean breeding program in Brazil in the last 47 years, and our data will help researchers to face the challenge of increase NUsE and Prot in the next few years.

Nitrogen rate impacts on tropical maize nitrogen use efficiency and soil nitrogen depletion in eastern and southern Africa

Sub-Saharan Africa is facing food security challenges due, in part, to decades of soil nitrogen (N) depletion. Applying N fertilizer could increase crop yields and replenish soil N pools. From 2010 to 2015, field experiments conducted in Embu and Kiboko, Kenya and Harare, Zimbabwe investigated yield and N uptake response of six maize (Zea mays L.) hybrids to four N fertilizer rates (0 to 160 kg N ha^-1) in continuous maize production systems. The N recovery efficiency (NRE), cumulative N balance, and soil N content in the upper 0.9 m of soil following the final harvest were determined at each N rate. Plant and soil responses to N fertilizer applications did not differ amongst hybrids. Across locations and N rates, NRE ranged from 0.4 to 1.8 kg kg^-1. Higher NRE values in Kiboko and Harare occurred at lower post-harvest soil inorganic N levels. The excessively high NRE value of 1.8 kg kg^-1 at 40 kg N ha^-1 in Harare suggested that maize hybrids deplete soil inorganic N most at low N rates. Still, negative cumulative N balances indicated that inorganic soil N depletion occurred at all N rates in Embu and Harare (up to - 193 and - 167 kg N ha^-1, respectively) and at the 40 kg N ha^-1 rate in Kiboko (- 72 kg N ha^-1). Overall, maize N uptake exceeded fertilizer N applied and so, while yields increased, soil N pools were not replenished, especially at low total soil N levels (< 10,000 kg N ha^-1 in top 0.9 m).