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Wheat is a globally important crop and one of the "big three" US field crops. But unlike the other two (maize and soybean), in the United States its development is commercially unattractive, and so its breeding takes place primarily in public universities. Troublingly, the incentive structures within these universities may be hindering genetic improvement just as climate change is complicating breeding efforts. "Business as usual" in the US public wheat-breeding infrastructure may not sustain productivity increases. To address this concern, we held a multidisciplinary conference in which researchers from 12 US (public) universities and one European university shared the current state of knowledge in their disciplines, aired concerns, and proposed initiatives that could facilitate maintaining genetic improvement of wheat in the face of climate change. We discovered that climate-change-oriented breeding efforts are currently considered too risky and/or costly for most university wheat breeders to undertake, leading to a relative lack of breeding efforts that focus on abiotic stressors such as drought and heat. We hypothesize that this risk/cost burden can be reduced through the development of appropriate germplasm, relevant screening mechanisms, consistent germplasm characterization, and innovative models predicting the performance of germplasm under projected future climate conditions. However, doing so will require coordinated, longer-term, inter-regional efforts to generate phenotype data, and the modification of incentive structures to consistently reward such efforts.

HighlightsThree contextual-based (CB) and two pixel-based (PB) models were evaluated to estimate ET of rainfed winter wheat.Instantaneous available energy estimation and ET upscaling impacted model performance.The CB models performed better at instantaneous and daily scales compared to the PB models.ET estimation biases increased during low vegetation and drier conditions, especially for the PB models.Abstract. Surface energy balance (SEB) models based on thermal remote sensing data are widely used in research applications to map evapotranspiration (ET) across various landscapes. However, their ability to capture ET from winter wheat remains underexplored, especially in practical applications such as integrated resource management and drought preparedness. Investigating winter wheat ET dynamics is important in agricultural regions such as the Southern Great Plains of the U.S., where winter wheat is extensively cultivated. The goal of this study was to evaluate the performance of five fully automated SEB models, three contextual-based (CB) and two pixel-based (PB), in estimating instantaneous and daily ET of winter wheat by comparing the model results with flux tower observations. The CB models included Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), and Triangular Vegetation Temperature (TVT). The PB models included Surface Energy Balance System (SEBS) and Two-Source Energy Balance (TSEB). Model evaluation during two winter wheat growing seasons (2016-2018) using 28 Landsat images showed that the instantaneous ET estimates from METRIC and TSEB had the smallest (RMSE = 0.14 mm h-1) and largest (RMSE = 0.27 mm h-1) errors, respectively. At the daily scale, SEBAL was the best performing model (RMSE = 1.0 mm d-1), followed by TVT (RMSE = 1.1 mm d-1), METRIC (RMSE = 1.2 mm d-1), SEBS (RMSE = 1.3 mm d-1), and TSEB (RMSE = 1.5 mm d-1). Overall, the CB models provided smaller errors than the PB models. Larger errors in daily ET estimation were observed during low vegetation and drier conditions, especially for the PB models. Keywords: Flux tower, Landsat, Southern Great Plains, Water use.

Phenotypic assessment of breeding population is important to identify robust lines for incorporating into future breeding programs. The objective of this study was to identify potential lines from a wheat (Triticum aestivum L.) population, based on their morpho-physiological traits, for improved heat tolerance. A subset of 100 lines of the double haploid (DH) population named “Buster”, developed from two successful Oklahoma wheat varieties (Billings and Duster), was used in the study. Two experiments were conducted one in a greenhouse and the other in growth chambers. Data on plant height, tiller number, leaf number, and photosynthetic pigments were collected from the greenhouse; whereas the data on physiological parameters (leaf net photosynthesis (Pn), transpiration (T), stomatal conductance (gs), intercellular carbon dioxide concentration (Ci), electron transport rate (ETR), Photosystem II efficiency (Fv'/Fm') and instantaneous water use efficiency (IWUE)) were collected from the growth chambers. Buster lines were significantly (P 2 m-2·s-1. The differences in leaf physiological parameters were more discernible under heat stress. This study provides a piece of baseline information on morpho-physiological characteristics of Buster lines, and identified lines can be used in future breeding programs for incorporating heat stress tolerance.

Made available in DSpace on 2020-12-10T20:07:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-09-15 Kansas Wheat Alliance Kansas Agricultural Experiment Station (KAES) The complex and interactive effects of genotype (G), environment (E), and management (M) can be a barrier to the development of sound agronomic recommendations. We hypothesize that long-term variety performance trials (VPT) can be used to understand these effects and improve regional recommendations. Our objective was to explore long-term VPT data to improve management and variety-selection recommendations using winter wheat (Triticum aestivum L.) in the U.S. central Great Plains as a case-study. Data of grain yield, variety, and trial management were collected from 748 wheat VPT conducted in the states of Colorado, Kansas, and Oklahoma over nineteen harvest years (2000-2018) and 92 locations, resulting in 97,996 yield observations. Using 30-yr cumulative annual precipitation and growing degrees days, we partitioned the study region into 11 contiguous sub-regions, which we refer to as growing adaptation regions (GAR). We used variance component analysis, gradient boosted trees, and conditional inference trees to explore the management and variety trait effects within each GAR. For the variety trait analysis, the VPT dataset was reduced to account for varieties for which 17 agronomic traits and 11 disease/insect reaction ratings were available (65,264 yield observations). GAR accounted for 46 % of the total variation in grain yield, M for 32 %, residuals (including interactions) for 13 %, year for 7 %, and G for 2 %. Conditional inference trees identified interactions among management practices and their effects on yield within each GAR. For instance, water regime was the most important practice influencing wheat yield in the semi-arid western portion of the study region, followed by sowing date and fungicide. In dryland trials, there was typically an interaction between fungicide, sowing date, and tillage system, depending on GAR. Other management practices (e.g. dual-purpose management, crop rotation, and tillage practice) also significantly affected yield, depending on GAR. The main variety trait associated with increased yields depended on region and management combination. For instance, drought tolerance was the most important trait in dryland trials while stripe rust tolerance was more relevant in irrigated trials in the semi-arid region. In this research, we demonstrated an approach that uses widely available long-term VPT data to improve management and variety selection recommendations and can be used in other regions and crops for which long-term VPT data are available. Kansas State Univ, Dept Agron, Manhattan, KS 66506 USA Kansas State Univ, Dept Stat, Manhattan, KS 66506 USA Kansas State Univ, Dept Plant Pathol, Throckmorton Hall, Manhattan, KS 66506 USA Colorado State Univ, Dept Soil & Crop Sci, Ft Collins, CO 80523 USA Oklahoma State Univ, Dept Plant & Soil Sci, Stillwater, OK 74078 USA Sao Paulo State Univ, Dept Crop Prod, Jaboticabal, SP, Brazil Sao Paulo State Univ, Dept Crop Prod, Jaboticabal, SP, Brazil Kansas Wheat Alliance: GAGR004805BG5828

Brachiaria and Cynodon are two of the most important pasture grasses worldwide. Computer model simulations can be used to study pasture species growth and physiological aspects to identify gaps of knowledge for genetic improvement and management strategies. The objective of this research was to compare the performance relative to calibrated parameters of the CROPGRO‐Perennial Forage Model (CROPGRO‐PFM) for simulating three different species (“Marandu” palisadegrass, “Convert HD 364®” brachiariagrass and “Tifton 85” bermudagrass) grown under similar management. The field experiment consisted of two harvest frequencies, 28 and 42 days, under irrigated and rainfed conditions. Data used to calibrate the model included regular forage harvests, plant‐part composition, leaf photosynthesis, leaf area index, light interception and plant nitrogen concentration. The simulation of biomass production of the three grasses presented d‐statistic values higher than 0.80, RMSE ranging from 313 to 619 kg/ha and ratio observed/simulated ranging 0.968 to 1.027. Harvest frequency treatments of 28 and 42 days were well simulated by the model. A sensitivity analysis was conducted to evaluate the most influential parameters needed for model calibration and to contrast the grasses, showing that the differences among the three grasses are mostly driven by plant‐part composition and assimilate partitioning among plant organs.

Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncertainties due to model structural differences per se and those due to parameter value uncertainties. Additionally, despite increasing use of Bayesian parameter estimation approaches with field-scale crop models, inadequate attention has been given to the full posterior distributions for estimated parameters. The objectives of this study were to quantify the impact of parameter value uncertainty on prediction uncertainty for modeling spring wheat phenology using Bayesian analysis and to assess the relative contributions of model-structure-driven and parameter-value-driven uncertainty to overall prediction uncertainty. This study used a random walk Metropolis algorithm to estimate parameters for 30 spring wheat genotypes using nine phenology models based on multi-location trial data for days to heading and days to maturity. Across all cases, parameter-driven uncertainty accounted for between 19 and 52% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 12 and 64%. This study demonstrated the importance of quantifying both model-structure- and parameter-value-driven uncertainty when assessing overall prediction uncertainty in modeling spring wheat phenology. More generally, Bayesian parameter estimation provided a useful framework for quantifying and analyzing sources of prediction uncertainty.

Parameter estimation is a critical step in successful application of dynamic crop models to simulate crop growth and yield under various climatic and management scenarios. Although inverse modeling parameterization techniques significantly improve the predictive capabilities of models, whether these approaches can recover the true parameter values of a specific genotype or cultivar is seldom investigated. In this study, we applied a Markov Chain Monte-Carlo (MCMC) method to the DSSAT dry bean model to estimate (recover) the genotype-specific parameters (GSPs) of 150 synthetic recombinant inbred lines (RILs) of dry bean. The synthetic parents of the population were assigned contrasting GSP values obtained from a database, and each of these GSPs was associated with several quantitative trait loci. A standard inverse modeling approach that simultaneously estimated all GSPs generated a set of values that could reproduce the original synthetic observations, but many of the estimated GSP values significantly differed from the original values. However, when parameter estimation was carried out sequentially in a stepwise manner, according to the genetically controlled plant development process, most of the estimated parameters had values similar to the original values. Developmental parameters were more accurately estimated than those related to dry mass accumulation. This new approach appears to reduce the problem of equifinality in parameter estimation, and it is especially relevant if attempts are made to relate parameter values to individual genes. Keywords: Crop models, Equifinality, Genotype-specific parameters, Markov chain Monte-Carlo, Parameterization.

Half of global wheat production occurs in irrigated cropping regions that face increasing water shortages. In these regions, seasonal forecasts could provide information about in-season climate conditions that could improve resource management, helping to save water and other inputs. However, seasonal forecasts have not been tested in irrigated systems. In this study, we show that seasonal forecasts have the potential to guide crop management decisions in fully irrigated systems (FIS), reduced irrigation systems (supplementary irrigation; SIS), and systems without irrigation (rainfed; RFS) in an arid environment. We found that farmers could gain an additional 2 USD ha − 1 season − 1 in net returns and save up to 26 USD ha − 1 season − 1 in N fertilizer costs with a hypothetical always-correct-season-type-forecast (ACF) in a fully irrigated system compared to simulated optimized N fertilizer applications. In supplementary irrigated systems, an ACF had value when deciding on sowing a crop (plus supplementary irrigation) of up to 65 USD ha − 1 season − 1 . In rainfed systems, this value was up to 123 USD ha − 1 when deciding whether or not to sow a crop. In supplementary irrigated and rainfed systems, such value depended on initial soil water conditions. Seasonal forecasts have the potential to assist farmers in irrigated, supplementary irrigated, and rainfed cropping systems to maximize crop profitability. However, forecasts currently available based on Global Circulation Models (GCM) and the El Nino Southern Oscillation (ENSO) need higher forecast skill before such benefits can be fully realized.

Pigeonpea ( Cajanus cajan (L.) Millsp.) is an important crop in Asia, Africa, Latin America and Caribbean. Despite pigeonpea's global importance there is a dearth of crop simulation models available for studying pigeonpea growth. The objectives of this study were to adapt the CSM-CROPGRO model for simulating pigeonpea growth and development through parameter modification and to illustrate the use of a sequential parameter estimation technique using a dataset from Gainesville, FL in 1984 and a dataset from India in 2003. The sequential approach to parameter estimation using a hybrid Metropolis–Hastings–Gibbs algorithm worked well at estimating physiologically plausible values for parameters with good correspondence to measured data. Reasonable results were obtained despite the use of approximations for measurement errors for the Gainesville dataset, which contained only treatment means. This study demonstrated that CROPGRO can be used to simulate the growth and development of pigeonpea. However, further testing of CROPGRO with more extensive pigeonpea datasets should be undertaken to confirm the accuracy of the parameter estimates developed from this study. Further theoretical and practical research into the parameter estimation approach is needed.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections., E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133.

All data are available via DOI http://doi.org/10.7910/DVN/ECSFZG, he data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security. The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

Many of the irrigated spring wheat regions in the world are also regions with high poverty. The impacts of temperature increase on wheat yield in regions of high poverty are uncertain. A grain yield–temperature response function combined with a quantification of model uncertainty was constructed using a multimodel ensemble from two key irrigated spring wheat areas (India and Sudan) and applied to all irrigated spring wheat regions in the world. Southern Indian and southern Pakistani wheat-growing regions with large yield reductions from increasing temperatures coincided with high poverty headcounts, indicating these areas as future food security ‘hot spots’. The multimodel simulations produced a linear absolute decline of yields with increasing temperature, with uncertainty varying with reference temperature at a location. As a consequence of the linear absolute yield decline, the relative yield reductions are larger in low-yielding environments (e.g., high reference temperature areas in southern India, southern Pakistan and all Sudan wheat-growing regions) and farmers in these regions will be hit hardest by increasing temperatures. However, as absolute yield declines are about the same in low- and high-yielding regions, the contributed deficit to national production caused by increasing temperatures is higher in high-yielding environments (e.g., northern India) because these environments contribute more to national wheat production. Although Sudan could potentially grow more wheat if irrigation is available, grain yields would be low due to high reference temperatures, with future increases in temperature further limiting production.

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.