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Crop yield results from the complex interaction between genotype, management, and environment. While farmers have control over what genotype to plant, and how to manage it, their decisions are often sub-optimal due to climate variability. Sub-seasonal climate predictions embrace the great potential to improve risk analysis and decision-making. However, adequate frameworks integrating future weather uncertainty to predict crop outcomes are lacking. Maize (Zea mays L.) yields are highly sensitive to weather anomalies, and very responsive to plant density (plants m-2), thus this variable could be optimized conditional to the seasonal prospects. The aims of this study were to (i) design a model that describes the yield-to-plant density (herein termed as yield-density) relationship as a function of weather variables, (ii) evaluate the predictive performance and analyze the sources of uncertainty, and (iii) provide probabilistic forecasts for predicting the economic optimum plant density (EOPD). We present a novel approach to enable decision-making in agriculture using sub-seasonal climate predictions and Bayesian modeling. This model provides crop management recommendations by accounting for various sources of uncertainty. A Bayesian hierarchical shrinkage model was fitted to the response of maize yield-density trials performed during the 2010-2019 period across 7 states in the United States, identifying the relative importance of key weather, crop, and soil variables. Tercile forecasts of precipitation and temperature from the International Research Institute (IRI) were used to forecast EOPD before the start of the season. The variables with the greatest influence on the yield-density relationship were weather anomalies, especially months with above-normal temperatures. Improvements on climate forecasting may also improve precision, as the coefficient of determination (R2) increased from 0.26 to 0.32 when weather forecasts were correct. This study may contribute to the development of decision-support tools that can trigger discussions between farmers and consultants about management strategies and their associated risks.

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.

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.

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.

Special Issue: Feeding the World: The Future of Plant Breeding Asymmetry of investment in crop research leads to knowledge gaps and lostopportunities to accelerate genetic gain through identifying new sources andcombinations of traits and alleles. On the basis of consultation with scientistsfrom most major seed companies, we identified several research areas withthree common features: (i) relatively underrepresented in the literature; (ii) highprobability of boosting productivity in a wide range of crops and environments;and (iii) could be researched in‘precompetitive’space, leveraging previousknowledge, and thereby improving models that guide crop breeding and man-agement decisions. Areas identified included research into hormones, recombi-nation, respiration, roots, and source–sink, which, along with new opportunitiesin phenomics, genomics, and bioinformatics, make it more feasible to explorecrop genetic resources and improve breeding strategies. The authors acknowledge the following scientists for helping to achieve a consensus on research bottlenecks with significant implications for crop improvement: Yann Manes (Syngenta), Jeremy Derory (Limagrain), Bruno Marty (BASF), and Hans Braun (recent director of the Global Wheat Program, CIMMYT). We acknowledge Renee Lafitte (Bill and Melinda Gates Foundation) for helpful feedback on the manuscript and Fatima Escalante for valuable assistance in coordinating edits to the manuscript and its formatting. M.R. acknowledges the International Wheat Yield Partnership (https://iwyp.org/) for establishing a precedent of integrating different research strands in wheat for targeted prebreeding and the Foundation for Food and Agricultural Research (https://foundationfar.org/) for supporting a translational research and prebreeding pipeline at CIMMYT to identify and stack climate resilience traits in wheat.

Because plants capture water and nutrients through roots, it was proposed that changes in root systems architecture (RSA) might underpin the 3-fold increase in maize (Zea mays L.) grain yield over the last century. Here we show that both RSA and yield have changed with decades of maize breeding, but not the crop water uptake. Results from X-ray phenotyping in controlled environments showed that single cross (SX) hybrids have smaller root systems than double cross (DX) hybrids for root diameters between 2465 µm and 181µm (P, Emerging opportunity to continue long-term genetic gain in maize yield in the US corn belt by improving the balance between canopy, root, and reproductive growth and development.

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.

A Crop Growth Model (CGM) is used to demonstrate a biophysical framework for predicting grain yield outcomes for Genotype by Environment by Management (G×E×M) scenarios. This required development of a CGM to encode contributions of genetic and environmental determinants of biophysical processes that influence key resource (radiation, water, nutrients) use and yield‐productivity within the context of the target agricultural system. Prediction of water‐driven yield‐productivity of maize for a wide range of G×E×M scenarios in the U.S. corn‐belt is used as a case study to demonstrate applications of the framework. Three experimental evaluations are conducted to test predictions of G×E×M yield expectations derived from the framework: (1) A maize hybrid genetic gain study, (2) A maize yield potential study, and (3) A maize drought study. Examples of convergence between key G×E×M predictions from the CGM and the results of the empirical studies are demonstrated. Potential applications of the prediction framework for design of integrated crop improvement strategies are discussed. The prediction framework opens new opportunities for rapid design and testing of novel crop improvement strategies based on an integrated understanding of G×E×M interactions. Importantly the CGM ensures that the yield predictions for the G×E×M scenarios are grounded in the biophysical properties and limits of predictability for the crop system. The identification and delivery of novel pathways to improved crop productivity can be accelerated through use of the proposed framework to design crop improvement strategies that integrate genetic gains from breeding and crop management strategies that reduce yield gaps.

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

Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (

Genetic gain in breeding programs depends on the predictive skill of genotype-to-phenotype algorithms and precision of phenotyping, both integrated with well-defined breeding objectives for a target population of environments (TPE). The integration of physiology and genomics could improve predictive skill by capturing additive and non-additive interaction effects of genotype (G), environment (E), and management (M). Precision phenotyping at managed stress environments (MSEs) can elicit physiological expression of processes that differentiate germplasm for performance in target environments, thus enabling algorithm training. Gap analysis methodology enables design of GxM technologies for target environments by assessing the difference between current and attainable yields within physiological limits. Harnessing digital technologies such as crop growth model-whole genome prediction (CGM-WGP) and gap analysis, and MSEs, can hasten genetic gain by improving predictive skill and definition of breeding goals in the U.S. maize production TPE. A half-diallel maize experiment resulting from crossing 9 elite maize inbreds was conducted at 17 locations in the TPE and 6 locations at MSEs between 2017 and 2019. Analyses over 35 families represented by 2367 hybrids demonstrated that CGM-WGP offered a predictive advantage (y) compared to WGP that increased with occurrence of drought as measured by decreasing whole-season evapotranspiration (ET; log(y) = 0.80(±0.6) − 0.006(±0.001) ×ET;r2= 0.59;df= 21). Predictions of unobserved physiological traits using the CGM, akin to digital phenotyping, were stable. This understanding of germplasm response to ET enables predictive design of opportunities to close productivity gaps. We conclude that enabling physiology through digital methods can hasten genetic gain by improving predictive skill and defining breeding objectives bounded by physiological realities.

ABSTRACTThe timing of crop development has significant impacts on management decisions and subsequent yield formation. A large intercontinental dataset recording the timing of soybean developmental stages was used to establish ensembling approaches that leverage both knowledge-based, human-defined models of soybean phenology and data-driven, machine-learned models to achieve accurate and interpretable predictions. We demonstrate that the knowledge-based models can improve machine learning by generating expert-engineered features. The collection of knowledge-based and data-driven models was combined via super learning to both improve prediction and identify the most performant models. Stacking the predictions of the component models resulted in a mean absolute error of 4.41 and 5.27 days to flowering (R1) and physiological maturity (R7), providing an improvement relative to the benchmark knowledge-based model error of 6.94 and 15.53 days, respectively, in cross-validation. The hybrid intercontinental model applies to a much wider range of management and temperature conditions than previous mechanistic models, enabling improved decision support as alternative cropping systems arise, farm sizes increase and changes in the global climate continue to accelerate.

Plant-breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeder’s equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeder’s equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimization of selection in breeding programs.

Over the last decade, society witnessed the largest expansion of agricultural land planted with drought tolerant (DT) maize (Zea maysL.) Dedicated efforts to drought breeding led to development of DT maize. Here we show that after two decades of sustained breeding efforts the rate of crop improvement under drought is in the range 1.0-1.6% yr−1, which is higher than rates (0.7% yr−1) reported prior to drought breeding. Prediction technologies that leverage biological understanding and statistical learning to improve upon the quantitative genetics framework will further accelerate genetic gain. A review of published and unpublished analyses conducted on data including 138 breeding populations and 93 environments between 2009 and 2019 demonstrated an average prediction skill (r) improvement around 0.2. These methods applied to pre-commercial stages showed accuracies higher that current statistical approaches (0.85 vs. 0.70). Improvement in hybrid and management choice can increase water productivity. Digital gap analyses are applicable at field scale suggesting the possibility of transition from evaluating hybrids to designing genotype x management (GxM) technologies for target cropping systems in drought prone areas. Due to the biocomplexity of drought, research and development efforts should be sustained to advance knowledge and iteratively improve models.HighlightCrop improvement rate in maize increased after implementation of drought breeding efforts. Harnessing crop, quantitative genetics and gap models will enable the transition from genetic evaluation to crop design.

Plants capture soil resources to produce the grains required to feed a growing population. Because plants capture water and nutrients through roots, it was proposed that changes in root systems architecture (RSA) underpin the three-fold increase in maize grain yield over the last century1,2,3,4. Within this framework, improvements in reproductive resilience due to selection are caused by increased water capture1. Here we show that both root architecture and yield have changed with decades of maize breeding, but not the water capture. Consistent with Darwinian agriculture5 theory, improved reproductive resilience6,7 enabled farmers increase the number of plants per unit land8,9,10, capture soil resources, and produced more dry matter and grain. Throughout the last century, selection operated to adapt roots to crowding, enabling reallocation of C from large root systems to the growing ear and the small roots of plants cultivated in high plant populations in modern agriculture.

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.

A successful strategy for prediction of crop yield that accounts for the effects of genotype, environment and their interactions with management will create many opportunities for enhancing the productivity of agricultural systems. Crop growth models (CGMs) have a history of application for crop management decision support. Recently, whole genome prediction (WGP) methodologies have been developed and applied in breeding to enable prediction of crop traits for new genotypes, and thus increased the size of plant breeding programs without expanding expensive field testing. The integration of a CGM into the algorithm for WGP, referred to as CGM-WGP, has opened up the potential for prediction of G × E × M interactions for breeding and product placement applications. The main objectives of this study were to extend CGM-WGP methodology to train models using data from multiple environments, and to evaluate, using both synthetic and experimental data from a maize drought breeding program, whether CGM-WGP methodology can enable improved phenotypic prediction when G × E interactions are an important determinant of performance. The CGM-WGP methodology was improved by 1) reformulating the model as a Bayesian generalized linear hierarchical model, and 2) by sampling the posterior distribution using a Metropolis-within-Gibbs sampling algorithm. The increased efficiency of the algorithm enabled the use of multiple environments and larger populations than those used in previous studies. Synthetic datasets included three environments and an empirical dataset included two environments contrasting for drought stress pattern and intensity. The empirical dataset included four double haploid populations expressing different levels of G × E interaction. Collectively, the prediction accuracy results for the empirical study indicate there were realized advantages in prediction accuracy for yield, in both the water limited and the not water limited environments, from the modeling of the G × E interactions by the CGM-WGP methodology relative to the reference method BayesA. Similarly, the difference in CGM-WGP accuracy relative to BayesA increased with decreasing similarity between the environment types utilized for training and evaluating the predictions. The synthesis provided in this work that encompasses crop physiology and modeling, quantitative genetics, genomic prediction and breeding, should stimulate a cross disciplinary dialogue towards building the next generation of prediction methodologies.

The competing demands of increasing grain yields to feed a growing population and decreasing nitrogen (N) fertilizer use and loss to the environment poses a grand challenge to farmers and society, and necessitates achieving improved N use efficiency (NUE) in cereal crops. Although selection for increased yield in maize has improved NUE over time, the present understanding of the physiological determinants of NUE and its key components hampers the design of more effective breeding strategies conducive to accelerating genetic gain for this trait. We show that maize NUE gains have been supported by more efficient allocation of N among plant organs during the grain filling period. Comparing seven maize hybrids commercialized between 1946 and 2015 from a single seed company in multiple N fertilizer treatments, we demonstrate that modern hybrids produced more grain per unit of accumulated N by more efficiently remobilizing N stored in stems than in leaves to support kernel growth. Increases in N fertilizer recovery and N harvest index at maturity were mirrored by a steady decrease in stem N allocation in this era study. These insights can inform future breeding strategies for continued NUE gains through improved conversion efficiency of accumulated plant N into grain yield.

Reproductive failure under drought in maize (Zea mays) is a major cause of instability in global food systems. While there has been extensive research on maize reproductive physiology, it has not been formalized in mathematical form to enable the study and prediction of emergent phenotypes, physiological epistasis and pleiotropy. We developed a quantitative synthesis organized as a dynamical model for cohorting of reproductive structures along the ear while accounting for carbon and water supply and demand balances. The model can simulate the dynamics of silk initiation, elongation, fertilization and kernel growth, and can generate well-known emergent phenotypes such as the relationship between plant growth, anthesis-silking interval, kernel number and yield, as well as ear phenotypes under drought (e.g. tip kernel abortion). Simulation of field experiments with controlled drought conditions showed that predictions tracked well the observed response of yield and yield components to timing of water deficit. This framework represents a significant improvement from previous approaches to simulate reproductive physiology in maize. We envisage opportunities for this predictive capacity to advance our understanding of maize reproductive biology by informing experimentation, supporting breeding and increasing productivity in maize.

Yield loss due to water deficit is ubiquitous in maize (Zea mays L.) production environments in the United States. The impact of water deficits on yield depends on the cropping system management and physiological characteristics of the hybrid. Genotypic diversity among maize hybrids in the transpiration response to vapor pressure deficit (VPD) indicates that a limited-transpiration trait may contribute to improved drought tolerance and yield in maize. By limiting transpiration at VPD above a VPD threshold, this trait can increase both daily transpiration efficiency and water availability for lateseason use. Reduced water use, however, may compromise yield potential. The complexity associated with genotype X environment X management interactions can be explored in a quantitative assessment using a simulation model. A simulation study was conducted to assess the likely effect of genotypic variation in limited-transpiration rate on yield performance of maize at a regional scale in the United States. We demonstrated that the limited-transpiration trait can result in improved maize performance in drought-prone environments and that the impact of the trait on maize productivity varies with geography, environment type, expression of the trait, and plant density. The largest average yield increase was simulated for drought-prone environments (135 g m(-2)), while a small yield penalty was simulated for environments where water was not limiting (-33 g m(-2)). Outcomes from this simulation study help interpret the ubiquitous nature of variation for the limited-transpiration trait in maize germplasm and provide insights into the plausible role of the trait in past and future maize genetic improvement.

Germplasm, genetics, phenotyping, and selection, combined with a clear definition of product targets, are the foundation of successful hybrid maize breeding. Breeding maize hybrids with superior yield for the drought-prone regions of the US corn-belt involves integration of multiple drought-specific technologies together with all of the other technology components that comprise a successful maize hybrid breeding programme. Managed-environment technologies are used to enable scaling of precision phenotyping in appropriate drought environmental conditions to breeding programme level. Genomics and other molecular technologies are used to study trait genetic architecture. Genetic prediction methodology was used to breed for improved yield performance for drought-prone environments. This was enabled by combining precision phenotyping for drought performance with genetic understanding of the traits contributing to successful hybrids in the target drought-prone environments and the availability of molecular markers distributed across the maize genome. Advances in crop growth modelling methodology are being used to evaluate the integrated effects of multiple traits for their combined effects and evaluate drought hybrid product concepts and guide their development and evaluation. Results to date, lessons learned, and future opportunities for further improving the drought tolerance of maize for the US corn-belt are discussed.

Although the importance of the critical period for establishing the maize (Zea mays L.) yield components of kernel number (KN) and kernel weight (KW) is well known, there is little detailed information on how early ear development during the critical period and lag phase are influenced by hybrid improvement or N stress. Seven hybrids commercialized between 1946 and 2015 were compared under a range of N stress created by five N fertilizer treatments: no N applied or a total of 220 kg N ha−1 where N was applied either at the four-leaf stage (V4), at flowering, or a combination of these two times. Ears (cobs plus kernels) were sampled five times at weekly intervals, from approximately 14 days before until 14 days after flowering, to determine dry weight, N concentration, and N content. The dynamics of these traits were modeled using an expolinear function and parameters estimated using a non-linear mixed model. Hybrid and N treatment influences on the exponential and linear phases of ear growth were determined by comparing parameters. The greatest impact of genotype or plant N status was realized in the linear phase when modern hybrids exhibited higher linear growth rates and ears accumulated progressively more N with increasing V4 stage N rate. Neither genotype nor N stress resulted in different ear growth or N accumulation rates during the exponential phase; however, modern hybrids exhibited a longer exponential phase duration. Use of expolinear function with parameters estimated within the non-linear mixed models enabled us to gain insight on development phases modified as the result of genetic improvement or affected by N stress. During the exponential phase, which overlaps with the lag phase when cell division is a key determinant of potential kernel weight and sink strength, neither hybrid era nor N treatments substantially impacted ear growth. Instead, the most important early ear growth characteristics to differentially impact final KN and KW at maturity were the duration of the lag phase and ear growth rates during the linear phase. Future research is suggested to unravel how the duration of the exponential phase is related to cell number and kernel size determination, and how these in turn affect kernel growth rate during the linear phase.

The availability of enhanced climate forecasts offers the potential for farmers to improve responses to climate variability. However, few studies have demonstrated actual effective uses of climate forecasts. Through interaction with regional agricultural experts, we evaluated the opportunities and constraints involved in the use of climate information in decision making regarding soybean farming in the Argentinean pampas. Our results showed that opportunities exist for the successful application of climate information, but consistent with previous research, there is a need to consider the broad and complex context influencing decisions, since climate is just one of the many factors affecting farmers' decisions. More importantly, we showed that adaptive management strategies proposed by experts in response to hypothetical climate scenarios produced diverging economic outcomes (both positive and negative). We hypothesized that inconsistency of the observed results could be due to a poor understanding by the agricultural experts of the impacts on regional climate of global climate signals (e.g. a given ENSO phase). An alternative hypothesis was that crop consultants had difficulties in anticipating the agronomic outcomes of management decisions made in response to a given climate forecast. Further research is needed to elucidate to what extent these hypotheses are valid. However, our results suggest that the mere availability of climate forecasts will not necessarily benefit growers. In order for there to be an improvement in the use of seasonal forecasts, appropriate interventions are necessary to enhance decision makers' understanding of the sources and impacts of climate variability, and of the consequences of different responses to a range of climate scenarios. Fil: Bert, Federico Esteban. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Cerealicultura; Argentina. Asociacion Argentina de Consorcios Regionales de Experimentacion Agricola.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Podestá, Guillermo Pablo. University of Miami; Estados Unidos Fil: Satorre, Emilio Horacio. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Cerealicultura; Argentina. Asociacion Argentina de Consorcios Regionales de Experimentacion Agricola.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Messina, Carlos D.. Pioneer Hi-bred International; Estados Unidos

Associations are investigated between yields of major crops in the Argentine Pampas (central-eastern Argentina) and El Nino–Southern Oscillation (ENSO) phase. For maize and sorghum, higher (lower) yield anomalies occur more frequently than expected by chance alone during warm (cold) ENSO events. For both crops, the depression of yields during cold events is, on average, larger and less variable than yield increases are during warm events. A yield decrease during cold events also is observed in soybean yields, although the effect of warm events is not statistically significant. There is a marginally significant tendency for low sunflower yields to occur less frequently than expected during cold events. Wheat, the only winter crop considered, did not show an association with ENSO. Precipitation anomalies during October–February (the period with strongest ENSO signal in the Pampas) are summarized through principal component analysis. Precipitation anomalies during November–January are significantly ...

The effectiveness of breeding strategies to increase drought resistance in crops could be increased further if some of the complexities in gene-to-phenotype (G → P) relations associated with epistasis, pleiotropy, and genotype-by-environment interactions could be captured in realistic G → P models, and represented in a quantitative manner useful for selection. This paper outlines a promising methodology. First, the concept of landscapes was extended from the study of fitness landscapes used in evolutionary genetics to the characterization of yield-trait-performance landscapes for agricultural environments and applications in plant breeding. Second, the E(NK) model of trait genetic architecture was extended to incorporate biophysical, physiological, and statistical components. Third, a graphical representation is proposed to visualize the yield-trait performance landscape concept for use in selection decisions. The methodology was demonstrated at a particular stage of a maize breeding programme with the objective of improving the drought tolerance of maize hybrids for the US Western Corn-Belt. The application of the framework to the genetic improvement of drought tolerance in maize supported selection of Doubled Haploid (DH) lines with improved levels of drought tolerance based on physiological genetic knowledge, prediction of test-cross yield within the target population of environments, and their predicted potential to sustain further genetic progress with additional cycles of selection. The existence of rugged yield-performance landscapes with multiple peaks and intervening valleys of lower performance, as shown in this study, supports the proposition that phenotyping strategies, and the directions emphasized in genomic selection can be improved by creating knowledge of the topology of yield-trait performance landscapes.

For the foreseeable future, plant breeding methodology will continue to unfold as a practical application of the scaling of quantitative biology. These efforts to increase the effective scale of breeding programs will focus on the immediate and long-term needs of society. The foundations of the quantitative dimension will be integration of quantitative genetics, statistics, gene-to-phenotype knowledge of traits embedded within crop growth and development models. The integration will be enabled by advances in quantitative genetics methodology and computer simulation. The foundations of the biology dimension will be integrated experimental and functional gene-to-phenotype modelling approaches that advance our understanding of functional germplasm diversity, and gene-to-phenotype trait relationships for the native and transgenic variation utilised in agricultural crops. The trait genetic knowledge created will span scales of biology, extending from molecular genetics to multi-trait phenotypes embedded within evolving genotype–environment systems. The outcomes sought and successes achieved by plant breeding will be measured in terms of sustainable improvements in agricultural production of food, feed, fibre, biofuels and other desirable plant products that meet the needs of society. In this review, examples will be drawn primarily from our experience gained through commercial maize breeding. Implications for other crops, in both the private and public sectors, will be discussed.

The transition from the vegetative to reproductive development is a critical event in the plant life cycle. The accurate prediction of flowering time in elite germplasm is important for decisions in maize breeding programs and best agronomic practices. The understanding of the genetic control of flowering time in maize has significantly advanced in the past decade. Through comparative genomics, mutant analysis, genetic analysis and QTL cloning, and transgenic approaches, more than 30 flowering time candidate genes in maize have been revealed and the relationships among these genes have been partially uncovered. Based on the knowledge of the flowering time candidate genes, a conceptual gene regulatory network model for the genetic control of flowering time in maize is proposed. To demonstrate the potential of the proposed gene regulatory network model, a first attempt was made to develop a dynamic gene network model to predict flowering time of maize genotypes varying for specific genes. The dynamic gene network model is composed of four genes and was built on the basis of gene expression dynamics of the two late flowering id1 and dlf1 mutants, the early flowering landrace Gaspe Flint and the temperate inbred B73. The model was evaluated against the phenotypic data of the id1 dlf1 double mutant and the ZMM4 overexpressed transgenic lines. The model provides a working example that leverages knowledge from model organisms for the utilization of maize genomic information to predict a whole plant trait phenotype, flowering time, of maize genotypes.

As scientists, we are at least as excited about the open questions—the things we do not know—as the discoveries. Here, we asked 15 experts to describe the most compelling open questions in plant cell biology. These are their questions: How are organelle identity, domains, and boundaries maintained under the continuous flux of vesicle trafficking and membrane remodeling? Is the plant cortical microtubule cytoskeleton a mechanosensory apparatus? How are the cellular pathways of cell wall synthesis, assembly, modification, and integrity sensing linked in plants? Why do plasmodesmata open and close? Is there retrograde signaling from vacuoles to the nucleus? How do root cells accommodate fungal endosymbionts? What is the role of cell edges in plant morphogenesis? How is the cell division site determined? What are the emergent effects of polyploidy on the biology of the cell, and how are any such “rules” conditioned by cell type? Can mechanical forces trigger new cell fates in plants? How does a single differentiated somatic cell reprogram and gain pluripotency? How does polarity develop de-novo in isolated plant cells? What is the spectrum of cellular functions for membraneless organelles and intrinsically disordered proteins? How do plants deal with internal noise? How does order emerge in cells and propagate to organs and organisms from complex dynamical processes? We hope you find the discussions of these questions thought provoking and inspiring., We asked 15 experts to address what they consider to be the most compelling open questions in plant cell biology and these are their questions.