Varying Atmospheric CO2 Mediates the Cold-Induced CBF-Dependent Signaling Pathway and Freezing Tolerance in Arabidopsis.

Changes in the stomatal aperture in response to CO2 levels allow plants to manage water usage, optimize CO2 uptake and adjust to environmental stimuli. The current study reports that sub-ambient CO2 up-regulated the low temperature induction of the C-repeat Binding Factor (CBF)-dependent cold signaling pathway in Arabidopsis (Arabidopsis thaliana) and the opposite occurred in response to supra-ambient CO2. Accordingly, cold induction of various downstream cold-responsive genes was modified by CO2 treatments and expression changes were either partially or fully CBF-dependent. Changes in electrolyte leakage during freezing tests were correlated with CO2's effects on CBF expression. Cold treatments were also performed on Arabidopsis mutants with altered stomatal responses to CO2, i.e., high leaf temperature 1-2 (ht1-2, CO2 hypersensitive) and ß-carbonic anhydrase 1 and 4 (ca1ca4, CO2 insensitive). The cold-induced expression of CBF and downstream CBF target genes plus freezing tolerance of ht1-2 was consistently less than that for Col-0, suggesting that HT1 is a positive modulator of cold signaling. The ca1ca4 mutant had diminished CBF expression during cold treatment but the downstream expression of cold-responsive genes was either similar to or greater than that of Col-0. This finding suggested that ßCA1/4 modulates the expression of certain cold-responsive genes in a CBF-independent manner. Stomatal conductance measurements demonstrated that low temperatures overrode low CO2-induced stomatal opening and this process was delayed in the cold tolerant mutant, ca1ca4, compared to the cold sensitive mutant, ht1-2. The similar stomatal responses were evident from freezing tolerant line, Ox-CBF, overexpression of CBF3, compared to wild-type ecotype Ws-2. Together, these results indicate that CO2 signaling in stomata and CBF-mediated cold signaling work coordinately in Arabidopsis to manage abiotic stress.

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.

Phosphorus Nutrition Affects Temperature Response of Soybean Growth and Canopy Photosynthesis.

In nature, crops such as soybean are concurrently exposed to temperature (T) stress and phosphorus (P) deficiency. However, there is a lack of reports regarding soybean response to T × P interaction. To fill in this knowledge-gap, soybean was grown at four daily mean T of 22, 26, 30, and 34°C (moderately low, optimum, moderately high, and high temperature, respectively) each under sufficient (0.5 mM) and deficient (0.08 mM) P nutrition for the entire season. Phosphorus deficiency exacerbated the low temperature stress, with further restrictions on growth and net photosynthesis. For P deficient soybean at above optimum temperature (OT) regimes, growth, and photosynthesis was maintained at levels close to those of P sufficient plants, despite a lower tissue P concentration. P deficiency consistently decreased plant tissue P concentration ≈55% across temperatures while increasing intrinsic P utilization efficiency of canopy photosynthesis up to 147%, indicating a better utilization of tissue P. Warmer than OTs delayed the time to anthesis by 8-14 days and pod development similarly across P levels. However, biomass partitioning to pods was greater under P deficiency. There were significant T × P interactions for traits such as plant growth rates, total leaf area, biomass partitioning, and dry matter production, which resulted a distinct T response of soybean growth between sufficient and deficient P nutrition. Under sufficient P level, both lower and higher than optimum T tended to decrease total dry matter production and canopy photosynthesis. However, under P-deficient condition, this decrease was primarily observed at the low T. Thus, warmer than optimum T of this study appeared to compensate for decreases in soybean canopy photosynthesis and dry matter accumulation resulting from P deficiency. However, warmer than OT appeared to adversely affect reproductive structures, such as pod development, across P fertilization. This occurred despite adaptations, especially the increased P utilization efficiency and biomass partitioning to pods, shown by soybean under P deficiency.

Random Forests for Global and Regional Crop Yield Predictions.

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

Effects of CO2 enrichment and drought pretreatment on metabolite responses to water stress and subsequent rehydration using potato tubers from plants grown in sunlit chambers.

Experiments were performed using naturally sunlit Soil-Plant-Atmosphere Research chambers that provided ambient or twice ambient CO2. Potato plants were grown in pots that were water sufficient (W), water insufficient for 12-18 days during both vegetative and tuber development stages (VR), or water insufficient solely during tuber development (R). In the ambient CO2 treatment, a total of 17 and 20 out of 31 tuber metabolites differed when comparing the W to the R and VR treatments, respectively. Hexoses, raffinose, mannitol, branched chain amino acids, phenylalanine and proline increased, although most organic acids remained unchanged or decreased in response to drought. Osmolytes, including glucose, branched chain amino acids and proline, remained elevated following 2 weeks of rehydration in both the ambient and elevated CO2 treatments, whereas fructose, raffinose, mannitol and some organic acids reverted to control levels. Failure of desiccated plant tissues to mobilize specific osmolytes after rehydration was unexpected and was likely because tubers function as terminal sinks. Tuber metabolite responses to single or double drought treatments were similar under the same CO2 levels but important differences were noted when CO2 level was varied. We also found that metabolite changes to water insufficiency and/or CO2 enrichment were very distinct between sink and source tissues, and total metabolite changes to stress were generally greater in leaflets than tubers.

Carbon dioxide diffusion across stomata and mesophyll and photo-biochemical processes as affected by growth CO2 and phosphorus nutrition in cotton.

Nutrients such as phosphorus may exert a major control over plant response to rising atmospheric carbon dioxide concentration (CO2), which is projected to double by the end of the 21st century. Elevated CO2 may overcome the diffusional limitations to photosynthesis posed by stomata and mesophyll and alter the photo-biochemical limitations resulting from phosphorus deficiency. To evaluate these ideas, cotton (Gossypium hirsutum) was grown in controlled environment growth chambers with three levels of phosphate (Pi) supply (0.2, 0.05 and 0.01mM) and two levels of CO2 concentration (ambient 400 and elevated 800µmolmol(-1)) under optimum temperature and irrigation. Phosphate deficiency drastically inhibited photosynthetic characteristics and decreased cotton growth for both CO2 treatments. Under Pi stress, an apparent limitation to the photosynthetic potential was evident by CO2 diffusion through stomata and mesophyll, impairment of photosystem functioning and inhibition of biochemical process including the carboxylation efficiency of ribulose-1,5-bisphosphate carboxylase/oxyganase and the rate of ribulose-1,5-bisphosphate regeneration. The diffusional limitation posed by mesophyll was up to 58% greater than the limitation due to stomatal conductance (gs) under Pi stress. As expected, elevated CO2 reduced these diffusional limitations to photosynthesis across Pi levels; however, it failed to reduce the photo-biochemical limitations to photosynthesis in phosphorus deficient plants. Acclimation/down regulation of photosynthetic capacity was evident under elevated CO2 across Pi treatments. Despite a decrease in phosphorus, nitrogen and chlorophyll concentrations in leaf tissue and reduced stomatal conductance at elevated CO2, the rate of photosynthesis per unit leaf area when measured at the growth CO2 concentration tended to be higher for all except the lowest Pi treatment. Nevertheless, plant biomass increased at elevated CO2 across Pi nutrition with taller plants, increased leaf number and larger leaf area.

Modeling and control for closed environment plant production systems.

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.