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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.

Drought invokes a variety of metabolic alterations in plant leaves to cope with stress situations. To understand the effects of CO2 and drought stress for leaf metabolic changes in potato [Solanum tuberosum (L)], two contrasting potato cultivars Harley Blackwell (HB, an early maturing, newer cultivar) and Snowden (SD, an established, full-season cultivar) were tested under water-limited conditions and CO2 enrichment. The results revealed that most of the drought-triggered metabolites were lower in HB compared to SD. However, HB showed quicker adjustments in the metabolic processes such as conversion of starch into soluble sugars and biosynthesis of phenylalanine and other compatible solutes at the early stages of the drought progression. Moreover, the existence of genotypic differences for leaf water potential (LWP) in response to CO2 enrichment was evident. Our study provides insights into the possible metabolic strategies of drought tolerance in potato cultivars under ambient and elevated CO2. [ABSTRACT FROM AUTHOR]

Changes in the stomatal aperture in response to CO 2 levels allow plants to manage water usage, optimize CO 2 uptake and adjust to environmental stimuli. The current study reports that sub-ambient CO 2 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 CO 2 . Accordingly, cold induction of various downstream cold-responsive genes was modified by CO 2 treatments and expression changes were either partially or fully CBF-dependent. Changes in electrolyte leakage during freezing tests were correlated with CO 2' s effects on CBF expression. Cold treatments were also performed on Arabidopsis mutants with altered stomatal responses to CO 2 , i.e., high leaf temperature 1-2 ( ht1-2 , CO 2 hypersensitive) and β-carbonic anhydrase 1 and 4 ( ca1ca4 , CO 2 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 βCA1/4 modulates the expression of certain cold-responsive genes in a CBF-independent manner. Stomatal conductance measurements demonstrated that low temperatures overrode low CO 2 -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 CO 2 signaling in stomata and CBF-mediated cold signaling work coordinately in Arabidopsis to manage abiotic stress.