Climate change vulnerability and risk

Beilfuss, Richard D.; Nhemachena, Charles

doi:10.4324/9781315282053-4

Cited by 9 publications

(15 citation statements)

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“…Water stress is one of the significant causes for crop losses worldwide, reducing average yields to 50 percent or even more; furthermore, possible global climate change scenario suggests a future upsurge in drought stress [10,11]. Therefore, breeding crop varieties for improved water use efficiency is of utmost importance [12].…”

Section: Introductionmentioning

confidence: 99%

Heterotic Effect of Different Cytoplasmic Combinations in Sunflower Hybrids Cultivated Under Diverse Irrigation Regimes

Tyagi

Dhillon

Kaur

et al. 2020

Plants

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The sunflower hybrids hold a narrow cytoplasmic diversity. Besides, the heterotic effect of wild cytoplasmic combinations of sunflower on important traits under water stress has not been explored in detail. Here, we evaluated the different sunflower cytoplasmic combinations in sunflower hybrids using cytoplasmic male sterile (CMS) sources as female parents. We used a total of sixteen sunflower genotypes representing twelve CMS lines from wild and conventional sources along with four restorer lines. Twelve CMS lines were crossed with four restorer lines to develop a total of 48 F1 hybrid combinations. The hybrids were evaluated under two different environments (i.e., regular irrigation and water stress) for morphophysiological, yield, and biochemical traits over two years. Heterotic effect for various CMS sources was evaluated on all of the three possible scales, namely, better-parent heterosis (BPH), mid-parent heterosis (MPH), and heterosis as percent of check (PSH-996). For better-parent and mid-parent heterosis, the CMS sources Helianthus annuus, Helianthus argophyllus, and Helianthus debilis demonstrated positive better-parent heterosis for seed yield, oil content, and oleic acid irrespective of the environment. However, the hybrid combinations of different sources when using the genotype RCR8297 as the restorer parent recorded maximum average returns. Furthermore, chlorophyll meter (SPAD) reading positively correlated with days to 50% flowering, days to maturity, plant height, and number of leaves per plant in both the environments. Overall, this study identified and compared the heterotic effect of the different cytoplasmic combinations in sunflower under water stress as well as under normal irrigation environments.

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Section: Introductionmentioning

confidence: 99%

Heterotic Effect of Different Cytoplasmic Combinations in Sunflower Hybrids Cultivated Under Diverse Irrigation Regimes

Tyagi

Dhillon

Kaur

et al. 2020

Plants

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“…Figure ), sometimes starting as early as October and extending well into April (Torrance, ). Rainfall is mainly dependent on the orientation and behaviour of the InterTropical Convergence Zone (ITCZ) , which is influenced by the change in pressure patterns to the north and south of the country (Beilfuss, ). Rainfall is also dependent on the passage of the upper westerly waves of mid‐latitude origin which enhance the intensity of rainfall during a given wet period.…”

Section: Introductionmentioning

confidence: 99%

Rainfall variability over Zimbabwe and its relation to large‐scale atmosphere–ocean processes

Mamombe

Kim

Choi

2016

Intl Journal of Climatology

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The relationship between interannual variability of Zimbabwe rainfall and atmosphere–ocean interaction processes is established using high‐resolution rainfall station datasets. Empirical orthogonal function (EOF) analysis is used to extract the spatiotemporal modes that explain most of the variations in the rainfall pattern. Results suggest strong homogeneity in the rainfall pattern that is dominantly described by EOF mode 1. The correlation analysis of mode 1 with sea surface temperatures (SSTs) supports the assumption that El Niño Southern Oscillation (ENSO) has a significant influence on Zimbabwe summer rainfall. Significant correlations exist between rainfall and SSTs over large regions of the tropical Indian and Pacific Oceans. The seasonal response of rainfall to the southern annular mode is reflected in both modes 1 and 2. Mode 1 suggests homogeneity in rainfall variations within the country and mode 2 describes the north–south rainfall fluctuations in the country. Although the magnitudes of their loadings are small, modes 3 and 4 extracted distinct rainfall regions within the country. This could be attributed to the use of more reliable, higher resolution rainfall data. These findings will assist to bring profound improvements to seasonal rainfall forecasting techniques around the country.

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“…varies from more than 1600 mm/year in several far northern highland areas to less than 600 mm/year in the water-stressed southern portion of the basin (Fig. 8d-f;Beilfuss, 2012). Our experiments shows that key hydrological parameters such are precipitation, evapotranspiration and runoff are highly variable across the basin, from year to year.…”

Section: The Gpr Model As a Topical Machine Learning Algorithm In Reg...mentioning

confidence: 57%

“…8a), the annual variability of TWS dynamics over the study region is represented by a variance of 24.755%. It can therefore be deduced that the strongest annual variability occurred over the central portion of the Zambezi Basin, which consists of notable sub-basins such as the Kabompo, Barotse, Kariba, Mupata, and Kafue, whose combined area is 546,251 km 2 (Beilfuss, 2012).…”

Section: The Influence Of River Discharge and Surface Water Estimates...mentioning

confidence: 99%

Identifying impacts of global climate teleconnection patterns on land water storage using machine learning

Kalu

Ndehedehe

Okwuashi

et al. 2023

Journal of Hydrology: Regional Studies

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Climate change vulnerability and risk

Cited by 9 publications

References 0 publications

Heterotic Effect of Different Cytoplasmic Combinations in Sunflower Hybrids Cultivated Under Diverse Irrigation Regimes

Heterotic Effect of Different Cytoplasmic Combinations in Sunflower Hybrids Cultivated Under Diverse Irrigation Regimes

Rainfall variability over Zimbabwe and its relation to large‐scale atmosphere–ocean processes

Identifying impacts of global climate teleconnection patterns on land water storage using machine learning

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