Genotype by environment interaction components underlying variations in root,sugar and white sugar yield in sugar beet (<Emphasis Type="Italic">Beta vulgaris</Emphasis> L.)

The success of plant breeding programs depends on the ability to provide farmers with genotypes with guaranteed superior performance in terms of yield across a range of environmental conditions. We evaluated 49 sugar beet genotypes in four different geographical locations in 2 years aiming to identify stable genotypes with respect to root, sugar and white sugar yields, and to determine discriminating ability of environments for genotype selection and introduce representative environments for yield comparison trials. Combinations of year and location were considered as environment. Statistical analyses including additive main effects and multiplicative interactions (AMMI), genotype main effects and genotype?×?environment interaction effects (GGE) models and AMMI stability value (ASV) were used to dissect genotype by environment interactions (GEI). Based on raw data, root, sugar and white sugar yields varied from 0.95 to 104.86, 0.15 to 20.81, and 0.09 to 18.45 t/ha across environments, respectively. Based on F-Gollob validation test, three interaction principal components (IPC) were significant for each trait in the AMMI model whereas according to F ratio (F_R) test two significant IPCs were identified for root yield and sugar yield and three for white sugar yield. For model diagnosis, the actual root mean square predictive differences (RMS PD) were estimated based upon 1000 validations and the AMMI-1 model with the smallest RMS PD was identified as the most accurate model with highest predictive accuracy for the three traits. In the GGE biplot model, the first two IPCs accounted for 60.52, 62.9 and 64.69% of the GEI variation for root yield, sugar yield and white sugar yield, respectively. According to the AMMI-1 model, two mega-environments were delineated for root yield and three for sugar yield and white sugar yield. The mega-environments identified had an evident ecological gradient from long growing season to intermediate or short growing season. Environment-focused scaling GGE biplots indicated that two locations (Ekbatan and Zarghan) were the most representative testing environments with discriminating ability for the three traits tested. Environmentally stable genotypes (i.e. G21, G28 and G29) shared common parental lines in their pedigree having resistance to some sugar beet diseases (i.e. rhizomania and cyst nematodes). The results of the AMMI model were partly in accord with the results of GGE biplot analysis with respect to mega-environment delineation and winner genotypes. The outcome of this study may assist breeders to save time and costs to identify representative and discriminating environments for root and sugar yield test trials and creates a corner stone for an accelerated genotype selection to be used in sweet-based programs.     

Pedological Investigation of a Litho-Toposequence in a Semi-Arid Region of Southwestern Iran

Eurasian Soil Science - A litho-toposequence of soils in a semi-arid region in southwestern Iran was investigated for their pedological properties. Nine representative pedons on different...     

Projected impacts of global warming on cropping calendar and water requirement of maize in a humid climate

In this research, the influence of climate change on maize cultivation was investigated and then, the possible solutions for adopting this natural hazard in the coasts of Caspian Sea in Iran was assessed. Weather data were generated for the 2011–2100 period using a statistical downscaling model under different climatic scenarios. Reference evapotranspiration (ETo) was calculated using a Neuro-fuzzy inference system. Cop water requirement was calculated by multiplying ETo by crop coefficients. Increased cardinal temperatures during 2011–2100 led to shifting in the planting date backward by 10–26 days. In addition, the projected global warming has a considerable effect on the duration of the vegetative growth stage resulting in earlier harvesting. However, the duration of the reproductive stage is less affected. Despite the obvious reduction in the length of the growing season, crop water requirement will increase by 10.6–15.3% in the future due to 1.64–28.4% increase in ETo. However, changing the cultivation time may lead to 11.2–264.5 m³ ha^?1 water saving during the whole cropping cycle through affecting both ETo and the crop growth cycle. This result demonstrates that management of the maize cropping calendar can be an effective way to achieve sustainable agriculture under future climate conditions in the study area.