A trait-based model ensemble approach to design rice plant types for future climate.

Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait-based multi-model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi-model simulations targeting enhanced productivity, and aggregated results into model-ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait-parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP-Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid-century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context-specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait-based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Simulated Optimum Sowing Date for Forage Pearl Millet Cultivars in Multilocation Trials in Brazilian Semi-Arid Region.

Forage production is primarily limited by weather conditions under dryland production systems in Brazilian semi-arid regions, therefore sowing at the appropriate time is critical. The objectives of this study were to evaluate the CSM-CERES-Pearl Millet model from the DSSAT software suite for its ability to simulate growth, development, and forage accumulation of pearl millet [Pennisetum glaucum (L.) R.] at three Brazilian semi-arid locations, and to use the model to study the impact of different sowing dates on pearl millet performance for forage. Four pearl millet cultivars were grown during the 2011 rainy season in field experiments conducted at three Brazilian semi-arid locations, under rainfed conditions. The genetic coefficients of the four pearl millet cultivars were calibrated for the model, and the model performance was evaluated with experimental data. The model was run for 14 sowing dates using long-term historical weather data from three locations, to determine the optimum sowing window. Results showed that performance of the model was satisfactory as indicated by accurate simulation of crop phenology and forage accumulation against measured data. The optimum sowing window varied among locations depending on rainfall patterns, although showing the same trend for cultivars within the site. The best sowing windows were from 15 April to 15 May for the Bom Conselho location; 12 April to 02 May for Nossa Senhora da Gloria; and 17 April to 25 May for Sao Bento do Una. The model can be used as a tool to evaluate the effect of sowing date on forage pearl millet performance in Brazilian semi-arid conditions.

A Predictive Model for Time-to-Flowering in the Common Bean Based on QTL and Environmental Variables.

The common bean is a tropical facultative short-day legume that is now grown in tropical and temperate zones. This observation underscores how domestication and modern breeding can change the adaptive phenology of a species. A key adaptive trait is the optimal timing of the transition from the vegetative to the reproductive stage. This trait is responsive to genetically controlled signal transduction pathways and local climatic cues. A comprehensive characterization of this trait can be started by assessing the quantitative contribution of the genetic and environmental factors, and their interactions. This study aimed to locate significant QTL (G) and environmental (E) factors controlling time-to-flower in the common bean, and to identify and measure G × E interactions. Phenotypic data were collected from a biparental [Andean × Mesoamerican] recombinant inbred population (F11:14, 188 genotypes) grown at five environmentally distinct sites. QTL analysis using a dense linkage map revealed 12 QTL, five of which showed significant interactions with the environment. Dissection of G × E interactions using a linear mixed-effect model revealed that temperature, solar radiation, and photoperiod play major roles in controlling common bean flowering time directly, and indirectly by modifying the effect of certain QTL. The model predicts flowering time across five sites with an adjusted r-square of 0.89 and root-mean square error of 2.52 d. The model provides the means to disentangle the environmental dependencies of complex traits, and presents an opportunity to identify in silico QTL allele combinations that could yield desired phenotypes under different climatic conditions.

Brief history of agricultural systems modeling.

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science.

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.

Developmental Studies of Maize-Infesting Picture-Winged Flies (Diptera: Ulidiidae).

Eleven species of picture-winged flies (Diptera: Ulidiidae) attack maize (Zea mays L.) in the Americas. Field and laboratory studies were used to determine developmental times on sweet corn ears for the three most common species attacking the crop in the United States, Chaetopsis massyla (Walker), Euxesta eluta Loew, and Euxesta stigmatias Loew. Egg plus larval stage developmental times were evaluated in early Spring and late Fall 2009, and late Spring 2010, by placing newly deposited eggs in protected ears in the field. Newly formed puparia were removed daily from cages and held in the laboratory to determine pupal developmental times. Developmental times were compared with flies reared on artificial diet in the laboratory. Ear- and diet-reared adults were held until their death to determine adult longevity. Developmental times, including for pupae from ear-reared larvae, were significantly affected by species and season. All three species required nearly twice as long to complete development in the late Fall compared to late Spring studies. Flies required 3-13 d longer to complete development on artificial diet than on ears. Euxesta eluta adults lived two to three times longer than the other species, and females of all species lived 10-15% longer than males. Species and seasonal developmental times are discussed in relation to ear developmental times and control strategies. It is estimated that 16-19 generations per year of all three fly species can develop in the field in the sweet corn production area of southern Florida.

Development of a QTL-environment-based predictive model for node addition rate in common bean.

KEY MESSAGE: This work reports the effects of the genetic makeup, the environment and the genotype by environment interactions for node addition rate in an RIL population of common bean. This information was used to build a predictive model for node addition rate. To select a plant genotype that will thrive in targeted environments it is critical to understand the genotype by environment interaction (GEI). In this study, multi-environment QTL analysis was used to characterize node addition rate (NAR, node day- 1) on the main stem of the common bean (Phaseolus vulgaris L). This analysis was carried out with field data of 171 recombinant inbred lines that were grown at five sites (Florida, Puerto Rico, 2 sites in Colombia, and North Dakota). Four QTLs (Nar1, Nar2, Nar3 and Nar4) were identified, one of which had significant QTL by environment interactions (QEI), that is, Nar2 with temperature. Temperature was identified as the main environmental factor affecting NAR while day length and solar radiation played a minor role. Integration of sites as covariates into a QTL mixed site-effect model, and further replacing the site component with explanatory environmental covariates (i.e., temperature, day length and solar radiation) yielded a model that explained 73% of the phenotypic variation for NAR with root mean square error of 16.25% of the mean. The QTL consistency and stability was examined through a tenfold cross validation with different sets of genotypes and these four QTLs were always detected with 50-90% probability. The final model was evaluated using leave-one-site-out method to assess the influence of site on node addition rate. These analyses provided a quantitative measure of the effects on NAR of common beans exerted by the genetic makeup, the environment and their interactions.

A potato model intercomparison across varying climates and productivity levels.

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.

An AgMIP framework for improved agricultural representation in IAMs.

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.

Estimation of nitrogen pools in irrigated potato production on sandy soil using the model SUBSTOR.

Recent increases in nitrate concentrations in the Suwannee River and associated springs in northern Florida have raised concerns over the contributions of non-point sources. The Middle Suwannee River Basin (MSRB) is of special concern because of prevalent karst topography, unconfined aquifers and sandy soils which increase vulnerability of the ground water contamination from agricultural operations--a billion dollar industry in this region. Potato (Solanum tuberosum L.) production poses a challenge in the area due to the shallow root system of potato plants, and low water and nutrient holding capacity of the sandy soils. A four-year monitoring study for potato production on sandy soil was conducted on a commercial farm located in the MSRB to identify major nitrogen (N) loss pathways and determine their contribution to the total environmental N load, using a partial N budget approach and the potato model SUBSTOR. Model simulated environmental N loading rates were found to lie within one standard deviation of the observed values and identified leaching loss of N as the major sink representing 25 to 38% (or 85 to 138 kg ha(-1) N) of the total input N (310 to 349 kg ha(-1) N). The crop residues left in the field after tuber harvest represented a significant amount of N (64 to 110 kg ha(-1) N) and posed potential for indirect leaching loss of N upon their mineralization and the absence of subsequent cover crops. Typically, two months of fallow period exits between harvest of tubers and planting of the fall row crop (silage corn). The fallow period is characterized by summer rains which pose a threat to N released from rapidly mineralizing potato vines. Strategies to reduce N loading into the groundwater from potato production must focus on development and adoption of best management practices aimed on reducing direct as well as indirect N leaching losses.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison.

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.

From flower to seed: identifying phenological markers and reliable growth functions to model reproductive development in the common bean (Phaseolus vulgaris L.).

The lack of dependable morphological indicators for the onset and end of seed growth has hindered modeling work in the common bean (Phaseolus vulgaris L.). We have addressed this problem through the use of mathematical growth functions to analyse and identify critical developmental stages, which can be linked to existing developmental indices. We performed this study under greenhouse conditions with an Andean and a Mesoamerican genotype of contrasting pod and seed phenotypes, and three selected recombinant inbred lines. Pods from tagged flowers were harvested at regular time intervals for various measurements. Differences in flower production and seed and pod growth trajectories among genotypes were detected via comparisons of parameters of fitted growth functions. Regardless of the genotype, the end of pod elongation marked the beginning of seed growth, which lasted until pods displayed a sharp decline in color, or pod hue angle. These results suggest that the end of pod elongation and the onset of color change are reliable indicators of important developmental transitions in the seed, even for widely differing pod phenotypes. We also provide a set of equations that can be used to model different aspects of reproductive growth and development in the common bean.

Putting mechanisms into crop production models.

Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30-40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO2 and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects.

Leaf photosynthesis and carbohydrates of CO2-enriched maize and grain sorghum exposed to a short period of soil water deficit during vegetative development.

Among C4 species, sorghum is known to be more drought tolerant than maize. The objective was to evaluate differences in leaf gas exchanges, carbohydrates, and two enzyme activities of these nicotinamide adenine dinucleotide phosphate-malic enzyme (NADP-ME) C4 subtype monocots in response to water deficit and CO2 concentration ([CO2]). Maize and sorghum were grown in pots in sunlit environmental-controlled chambers. Treatments included well watered (WW) and water stressed (WS) (water withheld at 26 days) and daytime [CO2] of 360 (ambient) and 720 (elevated) µmol mol⁻¹. Midday gas exchange rates, concentrations of nonstructural carbohydrates, and activities of sucrose-phosphate synthase (SPS) and adenosine 5'-diphosphoglucose pyrophosphorylase (ADGP) were determined for fully expanded leaf sections. There was no difference in leaf CO2 exchange rates (CER) between ambient and elevated [CO2] control plants for both maize and sorghum. After withholding water, leaf CER declined to zero after 8 days in maize and 10 days for sorghum. Sorghum had lower stomatal conductance and transpiration rates than maize, which resulted in a longer period of CER under drought. Nonstructural carbohydrates of both control maize and sorghum were hardly affected by elevated [CO2]. Under drought, however, increases in soluble sugars and decreases in starch were generally observed for maize and sorghum at both [CO2] levels. For stressed maize and sorghum, decreases in starch occurred earlier and were greater at ambient [CO2] than at elevated [CO2]. For maize, drought did not meaningfully affect SPS activity. However, a decline in SPS activity was observed for drought-stressed sorghum under both [CO2] treatments. There was an increase in ADGP activity in maize under drought for both [CO2] treatments. Such a response in ADGP to drought, however, did not occur for sorghum. The generally more rapid response of maize than sorghum to drought might be related to the more rapid growth of leaf area of maize.

Elevated CO2 increases water use efficiency by sustaining photosynthesis of water-limited maize and sorghum.

Maize and grain sorghum seeds were sown in pots and grown for 39 days in sunlit controlled-environment chambers at 360 (ambient) and 720 (double-ambient, elevated)µmol mol(-1) carbon dioxide concentrations [CO(2)]. Canopy net photosynthesis (PS) and evapotranspiration (TR) was measured throughout and summarized daily from 08:00 to 17:00h Eastern Standard Time. Irrigation was withheld from matched pairs of treatments starting on 26 days after sowing (DAS). By 35 DAS, cumulative PS of drought-stress maize, compared to well-watered plants, was 41% lower under ambient [CO(2)] but only 13% lower under elevated [CO(2)]. In contrast, by 35 DAS, cumulative PS of drought-stress grain sorghum, compared to well-watered plants, was only 9% lower under ambient [CO(2)] and 7% lower under elevated [CO(2)]. During the 27-35 DAS drought period, water use efficiency (WUE, mol CO(2)Kmol(-1)H(2)O), was 3.99, 3.88, 5.50, and 8.65 for maize and 3.75, 4.43, 5.26, and 9.94 for grain sorghum, for ambient-[CO(2)] well-watered, ambient-[CO(2)] stressed, elevated-[CO(2)] well-watered and elevated-[CO(2)] stressed plants, respectively. Young plants of maize and sorghum used water more efficiently at elevated [CO(2)] than at ambient [CO(2)], especially under drought. Reductions in biomass by drought for young maize and grain sorghum plants were 42 and 36% at ambient [CO(2)], compared to 18 and 14% at elevated [CO(2)], respectively. Results of our water stress experiment demonstrated that maintenance of relatively high canopy photosynthetic rates in the face of decreased transpiration rates enhanced WUE in plants grown at elevated [CO(2)]. This confirms experimental evidence and conceptual models that suggest that an increase of intercellular [CO(2)] (or a sustained intercellular [CO(2)]) in the face of decreased stomatal conductance results in relative increases of growth of C(4) plants. In short, drought stress in C(4) crop plants can be ameliorated at elevated [CO(2)] as a result of lower stomatal conductance and sustaining intercellular [CO(2)]. Furthermore, less water might be required for C(4) crops in future higher CO(2) atmospheres, assuming weather and climate similar to present conditions.

Short-term high temperature growth conditions during vegetative-to-reproductive phase transition irreversibly compromise cell wall invertase-mediated sucrose catalysis and microspore meiosis in grain sorghum (Sorghum bicolor).

Grain sorghum (Sorghum bicolor) crop yield is significantly compromised by high temperature stress-induced male sterility, and is attributed to reduced cell wall invertase (CWI)-mediated sucrose hydrolysis in microspores and anthers leading to altered carbohydrate metabolism and starch deficiency in pollen (Jain et al., 2007). Sorghum plants were grown under season-long ambient (30/20 degrees C day-time maximum/night-time minimum) or high temperature stress (HS, 36/26 degrees C) environments, or reciprocally transferred for 5-10 days between either temperature regimens through panicle and microspore developmental stages. Quantitative RT-PCR analyses for CWI gene SbIncw1, plasma membrane H(+)-ATPase (Mha1) and sugar transporter proteins (OsSUT3 and OsMST7 homologs in sorghum), starch deficiency and pollen sterility data are presented to confirm HS-sensitivity of pre- and post-meiotic stages of sorghum microsporogenesis. Heat stress-induced reduction in Incw transcriptional activity during microspore meiosis was irreversible despite return of optimal growth temperature conditions through further reproductive development.

Enhancement in leaf photosynthesis and upregulation of Rubisco in the C4 sorghum plant at elevated growth carbon dioxide and temperature occur at early stages of leaf ontogeny.

Rising atmospheric carbon dioxide (CO2) concentration and temperature will influence photosynthesis, growth and yield of agronomic crops. To investigate effects of elevated CO2 and high temperature on leaf gas exchanges, activities of Rubisco and phosphoenolpyruvate carboxylase (PEPC) and growth of grain sorghum (Sorghum bicolor L. Moench), plants were grown in controlled environments at day-time maximum/night-time minimum temperatures of 30/20°C or 36/26°C at ambient (350 µmol mol-1) or elevated (700 µmol mol-1) CO2. Gas-exchange rates, activities of Rubisco and PEPC and growth parameters (leaf, stem and total dry weights) were determined at different stages of leaf development. Between 6 and 25 days after leaf tip emergence, leaf carbon exchange rate (CER) of elevated CO2 plants was greater at 30/20°C and 36/26°C than that of ambient CO2 plants at the same temperatures. The positive response of CER to elevated CO2 was greater in young leaves than in old leaves. In young leaves, elevated CO2 enhanced Rubisco activity at 30/20°C and 36/26°C, whereas PEPC activity was not affected by elevated CO2 at 30/20°C but was marginally enhanced at 36/26°C. At 30/20°C, growth parameters were not affected by elevated CO2 until 50 days after sowing (DAS); at 36/26°C, they were progressively enhanced by elevated CO2 to as high as 49 to 62% by 50 DAS. Leaf CER and Rubisco activity were enhanced by elevated CO2 at early stages of leaf ontogeny for the C4 grain sorghum. Such enhancement should have a significant role in dry matter production under elevated CO2.

Response of bahiagrass carbon assimilation and photosystem activity to below optimum temperatures.

Photosynthesis and growth of tropical grasses are sensitive to cool season temperatures but information on the responsive mechanisms is limited in many species including bahiagrass (Paspalum notatum Flueggé). Therefore, an experiment was conducted in sunlit, controlled environment chambers to determine the effect of below optimum temperatures on leaf net photosynthesis (A) and chlorophyll fluorescence (F) and response to internal [CO2] (Ci) and photosynthetic photon flux density (PPFD) of A and F of bahiagrass. Five day/night temperatures of 14/6, 18/10, 22/14, 26/18 and 30/22°C were imposed from 55 to 100 days after transplanting for plants grown initially for 55 days at 30/22°C. Leaf A and F were measured from 1000 to 1400 hours between -1 to 35 days after imposing temperature treatments. Leaf A-F/Ci and A-F/PPFD response curves were measured between 11 and 20 days after start of temperature treatments. After 35 days of treatment, the cold acclimation response of leaf A was assessed by lowering temperature in all treatments to 6°C and measuring A and F for a 3-day period. Repeated-measures analysis showed significant effects of time, temperature and time × temperature. The reduction of A on the first day of cold shock was 64, 37, 61, 64 and 81% in plants previously grown at 14, 18, 22, 26 and 30°C, respectively, which indicates acclimation at 18°C. Below optimum temperature significantly lowered CO2-saturated net photosynthesis (Asat), carboxylation efficiency (CE) and electron transport rate (ETR) derived from A-F/Ci curves. Below optimum temperature also lowered light-saturated photosynthesis (Amax), Rd and ETR derived from A-F/PPFD curves. The relationship between φCO2 and φPSII showed that bahiagrass A was more sensitive than electron transport at below optimum temperatures, which may be associated with increased CO2 leakage and over-cycling of C4 acid cycle. The leaf-level photosynthesis parameters and their response functions will also help to improve algorithms for simulating forage growth under variable temperature conditions.