Search

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.

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.

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.

Development of predictive algorithms accounting for uncertainty in processes underpinning the maize (Zea mays L.) yield response to nitrogen (N) are needed in order to provide new N fertilization guidelines. The aims of this study were to unravel the relative importance of crop management, soil, and weather factors on both the estimate and the size of uncertainty (as a risk magnitude assessment) of the main components of the maize yield response to N: i) yield without N fertilizer (B0); ii) yield at economic optimum N rate (YEONR); iii) EONR; and iv) the N fertilizer efficiency (NFE) at the EONR. Combining Bayesian statistics to fit the N response curves and a machine learning algorithm (extreme gradient boosting) to assess features importance on the predictability of the process, we analyzed data of 730 response curves from 481 site-years (4297 observations) in maize N rate fertilization studies conducted between 1999 and 2020 in the United States and Canada. The EONR was the most difficult attribute to predict, with an average uncertainty of 50 kg N ha−1, increasing towards low ( 200 kg N ha−1) EONR expected values. Crop management factors such as previous crop and irrigation contributed substantially (∼50%) to the estimation of B0, but minorly to other components of the maize yield response to N. Weather contributed about two-thirds of explained variance of the estimated values of YEONR, EONR, and NFE. Additionally, weather factors governed the uncertainty (72% to 81%) of all components of the N response process. Soil factors provided a consistent but limited (10% to 23%) contribution to explain both expected N response as well as its associated uncertainties. Efforts to improve N decision support tools should consider the uncertainty of models as a type of risk, potential in-season weather scenarios, and develop probabilistic frameworks for improving this data-driven decision-making process of N fertilization in maize crop.

The introduction of winter canola (Brassica napus L.) into rotations with winter wheat (Triticum aestivum L.) in the United States (US) revealed economic and agronomic benefits as well as improved weed and pest control. Canola stand establishment during the fall and plant survival over the winter are critical to the success of this crop. The environment plays a key role influencing survival, but in-depth evaluation of meteorological factors has not been conducted. This research study aimed to: i) identify meteorological factors underpinning winter canola survival, ii) build probabilistic response models based on historical meteorological data for different severities of winter kill across the US, and iii) define areas of adaptation of current germplasm. A winter survival dataset was compiled from the National Winter Canola Variety Trials from 2003 until 2018 (190 site-years) and auxiliary meteorological data over the last 40 years. A regression tree analysis indicated that meteorological variables related to minimum temperature, fluctuating temperatures above and below 0°C, and windchill during the cold period were the main factors accounting for winter kill. Cold periods across all site-years were classified into three clusters: cold periods with high (96%), medium (70%), and low (28%) average plant winter survival. For 94 US sites, the probabilities of these conditions were calculated and summarized in a map that defined areas of adaptation: a large area south of 35° N latitude for the US was identified with greater potential for overwintering success. Based on the response under multiple meteorological conditions, four distinct genotype survival groups were identified (tolerant, semi-tolerant, semi-susceptible, and susceptible). Groups with a greater number of genotypes differ in the impact of meteorological conditions on survival for the medium cluster. In regions with more favorable conditions for overwintering success, farmers may be open to introducing this crop to diversify their farming system.

A plant trait to minimize the impact of drought on crop yield is limited-transpiration rate (TR) under high ambient vapor pressure deficit (VPD) so that soil water is conserved to sustain grain fill. Variation among maize (Zea mays L) hybrids has been identified for the existence of the limited-TR trait at high atmospheric VPD, and the VPD at which TR becomes limited. Further, it has been shown that the TR limitation at high VPD is related to plant hydraulic conductance, which may be due to differences in aquaporin expression. This paper reports studies to relate the TR response of 21 maize hybrids to treatment of leaves and intact plants with cycloheximide (CHX) and four aquaporin inhibitors: silver (AgNO3), gold (HAuCl4), zinc (ZnCl2), and mercury (HgCl2). There was no discrimination among hybrids based on treatment with Hg or CHX. Segregation between hybrids for response to increasing VPD corresponded with differences in leaf response to Ag and Au treatment and intact plant response to Zn. The highest correlation (r = 0.90) between VPD breakpoint and TR response to inhibitor was with Ag treatment of leaves. These results indicate that Ag may be an effective initial screen for expression of the limited-TR trait under high VPD. (C) 2014 Elsevier B.V. All rights reserved.

Late-season fertilizer N applications in maize (Zea mays L.) is a tactical agronomic practice that seeks to improve the synchrony between plant N demand and soil N supply. While this management practice can increase yield and limit unintended environmental impacts, a quantitative synthesis of the effects of late N applications on grain yield and resource use efficiency is lacking. A meta-analysis was conducted to identify patterns of yield response to late-N fertilization (post tenth-leaf, V10) across site-years studies. The goals of this study were to: 1) quantify the effect of late-season N fertilization on maize yield; and 2) identify an indicator for decision support of late-N fertilization. Published and unpublished sources from 1983 until 2018 were included in the meta-analysis (14 studies; n = 625 treatment means). Early-season (EN, ≈ 100% of fertilizer applied prior to the sixth-leaf, V6) and late-season (LN, 28% of N uptake at maturity occurring after silking) was positively related with yield increases for LN (+577 kg ha−1), and the contrast in REN between LN and EN was 0.04 kg kg−1. Environments prone to a greater N uptake during reproductive stages are proposed to be more suitable for late N applications. Prediction algorithms to inform tactical N management that can both increase productivity and sustainability could be developed provided adequate links between late-season N uptake and growing-season indicators could be established.

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.

Breeding for drought tolerance can benefit from a better understanding of possible responses of transpiration to various environmental variables. Temperature and vapour pressure deficit (VPD) are two important factors influencing stomatal conductance and transpiration. In this study, maize ( Zea mays L.) plants of four hybrids were grown under three day/night temperature regimes (30/26, 26/22 and 22/18 °C) in glasshouses, and the response of transpiration rate to changes in atmospheric VPD was measured at two different temperatures in a growth chamber. For all the hybrids examined, increases in transpiration rate with increasing VPD were similar and well described by a two-segment linear regression. There was little further increase in transpiration as VPD increased beyond a breakpoint. When measured at high temperature, the breakpoint in transpiration response to VPD occurred at significantly higher VPD and transpiration rate than at low temperature. The effect of growth temperature on transpiration was evident when plants were grown at low temperature (22/18 °C) and measured at higher temperature (30 °C). However, on the second day under the measurement temperature, the transpiration rate of these plants increased to the same level as those grown in higher day/night temperature environments. Limitation on transpiration at high VPD is a promising trait that could be incorporated into breeding programs to improve drought tolerance in maize.

Crop models use mathematical equations to simulate growth, development and yield as a function of weather, soil conditions and crop management. Such models integrate scientific knowledge from diverse agronomic disciplines, ranging from plant breeding to soil physics. Most crop models use one or more cultivar-specific parameters to identify differences in performance among cultivars. Until recently, however, there was little relation between cultivar-specific parameters and genotypes. The GeneGro model simulates the impact of seven genes on physiological processes in common bean (Phaseolus vulgaris L.), specifying cultivar differences through the presence or absence of the seven genes. The model was based on the bean model BEANGRO. GeneGro has now been incorporated into the cropping system model (CSM), which can simulate growth and development for more than 20 different crops, although the CSM-GeneGro version is currently implemented only for common bean and soybean [Glycine max (L.) Merr.]. Gene-based models can provide a well-structured linkage between functional genomics and crop physiology, especially as more genes are identified and their functions are clarified. Incorporating genetic information strengthens underlying physiological assumptions of the model, improving its utility for research in crop improvement, crop management, global change, and other fields. We first briefly review issues related to development of gene-based models, ranging from modeling approaches to data management. The CSM-GeneGro model is then used to show how specific genes can simulate both yield levels and yield variability for three locations in the USA. The model is also used to examine how single genes can affect crop response to global change. Gene-based modeling approaches could significantly enhance our ability to predict how global change will impact agricultural production, but modelers and physiologists will have to be proactive in accessing information and tools being developed in the plant genomics community.

The availability of long-lead ENSO-related climate forecasts has led many to speculate that such forecasts may benefit decision making in agriculture. To explore the conditions required for the effective use of climate forecasts, we conducted a pilot study focused on central-eastern Argentina. Historical records showed higher (lower) average precipitation during warm (cold) ENSO events in November–December. However, variability of the precipitation signal within ENSO phases was high. National-level yields of maize, soybeans and sorghum tended to be higher (lower) during warm (cold) events. A field survey was conducted to identify impediments for forecast adoption and learn how to communicate climate information. Most farmers surveyed know about ENSO, with the 1997–1998 event marking a “turning point” in their awareness of the phenomenon. Finally, various modeling approaches were used to explore outcomes of alternative management options (changes in crop management and land allocation) tailored to climate scenarios associated with each ENSO phase. Simulation exercises identified differences in optimal management between ENSO phases.

A risk assessment framework is presented to characterize the vulnerability of agricultural production systems to El Nino-southern oscillation (ENSO)-related climate variability. The framework was applied to current maize production systems in two locations (Pergamino and Pilar) in the Pampas of central-eastern Argentina. Climatic, agronomic, and economic models were linked to produce probability distributions of farm-level yields and net returns by ENSO phase. Generally, an enhanced chance of higher (lower) simulated maize yields existed during warm (cold) ENSO events. However, regional differences existed: the effect of warm events on yields was more marked in Pilar, but Pergamino showed a proportionally stronger response to cold events. The modeling framework allowed the exploration of outcomes of high and low scenarios of soil water availability at planting time and ENSO phase. High initial soil water availability in Pilar offset increased yield risks from dry conditions associated with cold ENSO events. Fluctuations of output prices were shown to have considerable influence on the risk associated with ENSO-related climate variability. Despite these general results, there was considerable overlap in yields and net returns for the various ENSO phases. This overlap has significant implications for the adoption of ENSO forecasts in agriculture. The risk assessment framework developed here is a necessary precursor to risk management studies that prescribe or describe possible responses to expected climate scenarios.

Using an on-farm approach, we investigated constraints to actual yield of sunflower in six agroecological zones within the Argentine Pampas during three growing seasons. In 249 large, grower-managed paddocks, we quantified a series of variables related to: (1) crop phenology, growth, and yield; (2) the physical and biological environment; and (3) management practices. Variation in yield among zones and seasons was analysed on the basis of four biologically-founded assumptions: (1) grain number accounts for a large proportion of the variation in yield; (2) grain number is associated with a photothermal coefficient, Q=R (T-Tb)−1, where R and T are average solar radiation and air temperature respectively, during the 50-day period bracketing anthesis; and Tb is a base temperature; (3) crop growth and yield are proportional to light interception, and therefore proportional to canopy ground cover; and (4) yield is proportional to the fraction of seasonal rainfall that occurs after anthesis. Average yield ranged from 1.1 to 2.7 t ha−1, grain number from 2400 to 5400 m−2, individual grain mass between 40 and 69 mg and grain oil concentration between 42 and 52%. Grain number accounted for 43% of the variation in average yield while Q accounted for 23% of the variation in grain number. Low yield was associated with deficient ground cover in 25% of the crops; part of the remaining variation in yield was accounted for by sets of measured variables particular to each zone, including soil shallowness, low available P, low initial water content, weeds and diseases — chiefly Verticillium wilt (Verticillium dahliae) and Sclerotinia head rot (Sclerotinia sclerotiorum). Across zones and seasons, the proportion of seasonal rainfall occurring after anthesis accounted for 28% of the variation in crop yield. A trade-off is highlighted whereby beneficial effects of rainfall that favours growth and yield may be offset by the detrimental effect of abundant moisture that favours major fungal diseases. We emphasised the value of combining experimental studies — which provide biological background in the form of working hypotheses — with on-farm research that realistically quantifies yield response to key factors.

Climate variability leads to economic and food security risks throughout the world because of its major influences on agriculture. Accurate forecasts of climate 3‐6 months ahead of time can potentially allow farmers and others in agriculture to make decisions to reduce unwanted impacts or take advantage of expected favorable climate. However, potential benefits of climate forecasts vary considerably because of many physical, biological, economic, social, and political factors. The purpose of this study was to estimate the potential economic value of climate forecasts for farm scale management decisions in one location in the Southeast USA (Tifton, GA; 3123 0 N; 8331 0 W) for comparison with previously-derived results for the Pampas region of Argentina. The same crops are grown in both regions but at different times of the year. First, the expected value of tailoring crop mix to El Nino-Southern Oscillation (ENSO) phases for a typical farm in Tifton was estimated using crop models and historical daily weather data. Secondly, the potential values for adjusting management of maize (Zea maize L.) to different types of climate forecasts (perfect knowledge of (a) ENSO phase, (b) growing season rainfall categories, and (c) daily weather) were estimated for Tifton and Pergamino, Argentina (3355 0 S; 6033 0 W). Predicted benefits to the farm of adjusting crop mix to ENSO phase averaged from US$ 3 to 6 ha 1 over all years, depending on the farmer’s initial wealth and aversion to risk. Values calculated for Argentina were US$ 9‐15 for Pergamino and up to US$ 35 for other locations in the Pampas. Varying maize management by ENSO phase resulted in predicted forecast values of US$ 13 and 15 for Tifton and Pergamino, respectively. The potential value of perfect seasonal forecasts of rainfall tercile on maize profit was higher than for ENSO-based forecasts in both regions (by 28% in Tifton and 70% in Pergamino). Perfect knowledge of daily weather over the next season provided an upper limit on expected value of about US$ 190 ha 1 for both regions. Considering the large areas of field crop production in these regions, the estimated economic potential is very high. However, there are a number of challenges to realize these benefits. These challenges are generally related to the uncertainty of climate forecasts and to the complexities of agricultural systems. © 2000 Elsevier Science B.V. All rights reserved.

The El Nino-Southern Oscillation (ENSO) contributes to the vulnerability of crop production to climate variability in the Pampas region of Argentina. Predictability of regional climate anomalies associated with ENSO may provide opportunities to tailor decisions to expected climate, either to mitigate expected adverse conditions or to take advantage of favorable conditions. Model analysis was used to explore the potential for tailoring land allocation among crops to ENSO phases at the farm scale in two sub-regions of the Pampas. The model identifies as a function of risk preferences and initial wealth the crop mix that maximizes expected utility of wealth at the end of a 1-year decision period based on current costs and prices, and crop yields simulated for each year of historical weather. The model reproduced recent land allocation patterns at the district scale under moderate risk aversion, and predicted increasing diversification with increasing risk aversion. Differences in land allocation among ENSO phases were consistent with known climate response to ENSO, and crop response to water availability. Tailoring land allocation to ENSO phase increased mean net farm income between US$5 and $15 ha −1 year −1 relative to optimizing the crop mixture for all years, depending on location, risk aversion and initial wealth. The relationship between potential value of ENSO information and risk aversion was not monotonic, and differed between locations. Crop mix and information value also varied with crop prices and initial soil moisture. There are potential financial benefits of applying this approach to tailoring decisions to ENSO phases.