Sorghum [Sorghum bicolor (L.) Moench] is a very important crop in the arid and semi-arid tropics of India and African subcontinent. In the process of release of new cultivars using multi-location data major emphasis is being given on the superiority of the new cultivars over the ruling cultivars, while very less importance is being given on the genotype 9 environment interaction (GEI). In the present study, performance of ten Indian hybrids over 12 locations across the rainy seasons of 2008 and 2009 was investigated using GGE biplot analysis. Location attributed higher proportion of the variation in the data (59.3-89.9%), while genotype contributed only 3.9-16.8% of total variation. Genotype 9 location interaction contributed 5.8-25.7% of total variation. We could identify superior hybrids for grain yield, fodder yield and for harvest index using biplot graphical approach effectively. Majority of the testing locations were highly correlated. 'Which-wonwhere' study partitioned the testing locations into three mega-environments: first with eight locations with SPH 1606/1609 as the winning genotypes; second megaenvironment encompassed three locations with SPH 1596 as the winning genotype, and last mega-environment represented by only one location with SPH 1603 as the winning genotype. This clearly indicates that though the testing is being conducted in many locations, similar conclusions can be drawn from one or two representatives of each mega-environment. We did not observe any correlation of these mega-environments to their geographical locations. Existence of extensive crossover GEI clearly suggests that efforts are necessary to identify location-specific genotypes over multi-year and -location data for release of hybrids and varieties rather focusing on overall performance of the entries.

Sorghum [Sorghum bicolor (L.) Moench] is a drought‐resilient crop, grown extensively in semiarid tropics of the world. To understand the scenario of sorghum cultivation across the world, trends in area and yield gain and associated changes in yield stability were analyzed in the top 10 sorghum‐producing countries from 1970 to 2009. Asian countries and the United States recorded a large drop in harvested area. Grain yield levels increased substantially in all the countries except Sudan. Relative to yield level of 1970, sorghum productivity increased annually at 0.96% yr−1 across the top 10 countries analyzed. China (100.9 kg ha−1 yr−1) and Nigeria (48.6 kg ha−1 yr−1) experienced phenomenal yield gain before reaching a plateau. Overall yield gain was not associated with increased yield stability in a majority of countries except Ethiopia. In fact, in China and India (post‐rainy‐season sorghum), the yield variability increased over time. Genetic gain for grain yield over years in the Indian sorghum improvement program was prominent in rainy‐season hybrid trials (18.5 kg ha−1 yr−1), whereas both in post‐rainy‐season hybrid and varietal trials it was insignificant. Much progress in rainy‐season variety trials after 1985 was not observed. Across years in India, the gap between potential and farm yield declined 0.32% yr−1 among rainy‐season cultivars and 0.46% yr−1 among post‐rainy‐season cultivars. The analysis reveals that though substantial progress has been made towards yield gain, this was not represented by increased production because of extensive loss of the sorghum area to other remunerative crops.

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SUMMARYSorghum [Sorghum bicolor (L.) Moench] grown in India is of two adaptive types: rainy and post-rainy. The post-rainy sorghum is predominantly consumed by humans. While releasing new cultivars through multi-location testing, major emphasis is given to the superiority of new cultivars over existing cultivars, with very little emphasis on the genotype × environment interaction (GEI). To understand the complexity of GEI in post-rainy sorghum testing location trials, the multi-location evaluation data of two post-rainy seasons (2009/10 and 2010/11) under the All India Coordinated Sorghum Improvement Project were analysed. In both years, location explained the highest proportion of total sum of squares followed by the GEI effect and main effect of genotype. Additive main effects and multiplicative interaction (AMMI), stability values (ASV) and genotype + genotype × environment interaction (GGE) instability values recorded high correlation resulting in identification of the best performing cultivars. However, the rank correlations were lower, though still significant. A mixture of crossover and non-crossover GEI was a common occurrence in both years. ‘Which-won-where’ analysis suggested the existence of four possible mega-environments (ME) among post-rainy testing locations, with a few non-informative locations within ME. Mega-environments are characterized by soil type, rainfall pattern and moisture conservation practices. The present study indicated the possibility of reducing the number of test locations by eliminating non-representative highly correlated locations and suggested the need to breed for location-specific genotypes rather than genotypes with wider adaptability.