Geographical variation of growth habit was studied for 749 landraces from various parts of the world, with special reference to their adaptation and ecogeographical differentiation. The total frequency of spring‐type landraces was 49.9%, and varied between localities. Spring‐type landraces were frequent in two distinct areas where the average January temperature was either below ‐7°C or above 4°C, with winter‐type landraces in areas from ‐7°C to 4°C. These results indicated that geographical variation of growth habit is closely related to the degree of winter coldness. An analysis of the Vrn genotype for 216 spring‐type landraces demonstrated the uneven distribution of four Vrn genes, with Vrn4 being the least frequent. The adaptive Vrn genotype was different between localities. Genotypes carrying Vrn‐A1 and additional Vrn gene(s) were frequent in two distinct areas where the average January temperature was either below ‐7°C or over 10°C, while genotypes with any of three Vrn genes, except Vrn‐A1, adapted to areas with temperatures from 4°C to 10°C. Therefore, it was concluded that the adaptability of wheat landraces differed depending on their growth habit and Vrn genotype, and that ecotypes with different Vrn genotypes were allopatrically distributed as a result of adaptation to different winter temperature. However, the differential distribution of Vrn‐B1, Vrn‐D1 and Vrn4 could not be explained by their adaptability, and might reflect the polyphyletic origin of common wheat.

This study models the relationship between rainfall variability parameters and millet (Pennisetum americanum L.) and sorghum (Sorghum bicolor L. Moench.) yields in the Sudan Savanna ecological zone of Nigeria. Daily rainfall data recorded by the nearby stations for the period between 1981 and 2010 as well as millet and sorghum yield data were used as inputs in the model. It attempts to develop model for predicting millet and sorghum yields based on rainfall variables. The analytical tools used in developing and testing the model performance include ogive of cumulative pentad rainfall, product-moment correlation coefficient (r), stepwise multiple regression analysis and coefficient of determination (R 2 ). The study produced four yield forecast models; three for millet at Kano, Katsina and Potiskum and the last for sorghum at Potiskum. Model accounted for 61.7% of variation in millet yield due to total annual rainfall and 68.1% in sorghum yield due to total rainfall in may assume the best-fitted yield forecast models. The unaccounted variation of 38.3% in millet yield and 31.9% in sorghum indicate other factors could influence the yield variability of the two crops. It is recommended that based on holding annual and may totals rainfall constant, the two best-fitted crop yield models should be used for planning and forecasting the yield of millet and sorghum in the study area.