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Modern agriculture, based on biparental crop varieties have contributed tremendously to the world’s food supply. However, the strategy is also being challenged due to stagnation in yield growth, climate change, susceptibility to biotic and abiotic stresses etc. Biparental crossing, the conventional cereal breeding approach, is inherently limited in its ability to fully harness the rich genetic diversity available within a crop species. This limitation stems from the restricted number of parental lines involved, which restricts the pool of desirable traits that can be combined. In contrast, cutting-edge multi-parental crossing strategies possess immense potential for generating superior trait combinations by tapping into a vastly broader genetic pool. However, despite the several advantages of this approach, its full potential has not been adequately exploited. The existing research on the development of multi-parent advanced generation inter-cross (MAGIC) populations in crops such as rice, maize, and sorghum has primarily focused on the populations themselves, lacking robust demonstrations of the potential advantages of this approach over biparental crossing in terms of developing superior crop varieties. This study aimed to develop post-rainy season sorghum genotypes with enhanced yield potential and improved tolerance to drought, shoot fly, and charcoal rot through the utilization and demonstration of a multi-parent crossing approach. 17 founder lines were utilized to generate four 8-way crosses. The performance of the resulting progeny was systematically evaluated across multiple locations. The results revealed that the 8-way cross-derived lines exhibited remarkable superiority in both grain and stover yields, outperforming not only the 2-way and 4-way cross derivatives but also their founder parents. Notably, the 8-way cross-derived lines demonstrated substantial yield advantages of over 70% and 30% in grain and stover production, respectively, compared to the bi-parent crosses. These lines also displayed enhanced drought tolerance and improved resistance against key insect pests and diseases. Specifically, two 8-way cross-derived lines, S22086RV and S22085RV, significantly outperformed the national check cultivar CSV 29R, with nearly 70% and 60% higher grain yields, and over 30% and 15% greater stover yields, respectively. Importantly, these high-performing lines also exhibited exceptional drought stress tolerance, characterized by high transpiration rate, transpiration efficiency, shoot biomass, harvest index, and grain yield coupled with low total water use, as well as resistance against shoot fly (< 15% dead hearts) and charcoal rot (< 10 charcoal rot index). These versatile, stress-resilient lines hold immense promise as valuable genetic resources to drive further crop improvement and the development of superior post-rainy sorghum varieties. This innovative breeding strategy demonstrates significant potential for transforming post-rainy sorghum cultivation, particularly in contexts constrained by limited phenotypic diversity that impedes progress.

Aim The search for possible factors influencing the spatial variation of grass quality is an important step towards understanding the distribution of herbivores, as well as a step towards identifying crucial areas for conservation and restoration. A number of studies have shown that grass quality at a regional scale is influenced by climatic variables. At a local scale, site factors and their interaction are considered important. In this study, we aimed at examining environmental correlates of grass quality at a local scale. The study also sought to establish if biotic factors interact significantly with abiotic factors in influencing a variation in grass quality. Location The study area is located in the Kruger National Park of South Africa. The study area stretches from west (22°49' S and 31°01' E) to east, (22°44' S and 31°22' E) covering an area of about 25 x 6 km in the far northern region of the Kruger National Park. Methods We collected environmental data such as soil texture, percentage grass cover and biomass as well as grass samples for chemical analysis from specific locations in the study area. In addition, a digital elevation model (DEM) with a resolution of 5 m was used to derive elevation, slope and aspect using a geographic information system (GIS), which were related to grass quality. We used correlation analysis and ANOVA to relate environmental variables to grass quality. Multivariate analysis techniques were used to simultaneously analyse and explore the complex interactions between variables. Results and conclusions Our results indicate that there is a significant relationship between grass quality parameters and site-specific factors such as slope, altitude, percentage grass cover, aspect and soil texture. Relatively, percentage grass cover and soil texture were more critical in explaining a variation in grass quality. Plant characteristics such as species type interact significantly with slope, altitude and geology in influencing nutrient distribution. The results of this study may give a better insight on foliar nutrient distribution patterns at a landscape scale in savanna rangelands. Furthermore, the results of this study may help in the selection of ancillary information, which could be used in conjunction with other data such as remotely sensed data to map grass quality - an important step towards understanding the distribution and feeding patterns of wildlife. However, we acknowledge that this study is based on one seasonal snapshot, therefore some slightly different findings may be obtained during other times of the year. Nevertheless, the study has revealed that under the conditions experienced during the study period, nutrient distribution varies with varying biotic and abiotic factors.

Only scattered information is available on the tillage and nutrient management information for the sesame crop following rice in the literature. Sesame as an edible oil yielding crop with high levels of unsaturated fatty acids has high international demand due to superior health benefits. Being a small seeded crop, it requires standard tillage and nutrient management to obtain optimum productivity under rice fallow ecologies. As a sequential crop after rice harvest, the tillage and nutrient management practices followed for the preceding rice have astounding effects on the succeeding sesame crop. To better understand and manipulate the agro ecology in the rice fallow culture, it is necessary to study the behaviour of sesame cultivars, in relation to the tillage requirements and macro nutrient factors that have a bearing on the productivity. The aim of this work was to evaluate the productivity of rice fallow sesame in the southern plateau and hills regions of the Indian sub-continent (Tamil Nadu) with a hypothesis that tillage and nutrient management would immensely benefit the sesame crop. Field experiments were conducted at TNAU, Tamil Nadu Rice Research Institute, Aduturai, Tamil Nadu during 2019-2020 and 2020-2021 with tillage practices (reduced tillage, conventional tillage and zero tillage) and fertilizer doses (zero percent RDF, 25% RDF, 50% RDF, 75% RDF and 100% RDF) in a split plot design replicated thrice. The results have clearly indicated that the performance of rice fallow sesame was poor under zero till conditions as the sesame crop is poorly adapted leading to a yield penalty up to 68%. A total of 75% RDF has yielded statistically similar yield to that of 100% RDF to the rice fallow sesame. Further, neither the oil content nor the fatty acid composition was modified by tillage and nutrient management regimes.

Post-rainy season sorghum (Sorghum bicolor (L.) Moench) yields are often constrained by cold stress. Cold tolerance is a prerequisite for adaptation to post-rainy season. A study was conducted to develop a field screening technique for cold tolerance, and to identify traits that discriminate genotypes to cold response. A set of rainy and post-rainy season adapted sorghum genotypes were sown in 2010 – 2011 and 2011 – 2012 in 3 dates of sowing in post-rainy seasons from third week of October to second week of November. The average minimum temperature during a 30-day period after the initial 40-day vegetative growth was lower in October sowing, moderate in November sowing and high in December sowing. The panicle harvest index (PHI) was found to be sensitive to low temperatures and was identified as a stable trait for genetic discrimination. Reduction in PHI can be indicative of failure to set seed or poor grain filling. The genotypes bred for rainy season ICSB 52, ICSR 149 and ICSR 93034 exhibited reduced PHI and lighter seed in cold-prone environments, indicating that their sensitivity is also well reflected in the yields of ICSB 52 and ICSR 149. However, the genotypes bred for post-rainy season, Dagadi solapur, SPV 1411 and M 35-1, were not affected by cold, as indicted by their higher PHI and relatively less reduction in seed weight. It was suggested that PHI and seed weight together can be used as proxies in selecting for reproductive stage cold tolerance in sorghum.

Data associated with germplasm collections are typically large and multivariate with a considerable number of descriptors measured on each of many accessions. Pattern analysis methods of clustering and ordination have been identified as techniques for statistically evaluating the available diversity in germplasm data. While used in many studies, the approaches have not dealt explicitly with the computational consequences of large data sets (i.e. greater than 5000 accessions). To consider the application of these techniques to germplasm evaluation data, 11328 accessions of groundnut (Arachis hypogaea L) from the International Research Institute for the Semi-Arid Tropics, Andhra Pradesh, India were examined. Data for nine quantitative descriptors measured in the rainy and post-rainy growing seasons were used. The ordination technique of principal component analysis was used to reduce the dimensionality of the germplasm data. The identification of phenotypically similar groups of accessions within large scale data via the computationally intensive hierarchical clustering techniques was not feasible and non-hierarchical techniques had to be used. Finite mixture models that maximise the likelihood of an accession belonging to a cluster were used to cluster the accessions in this collection. The patterns of response for the different growing seasons were found to be highly correlated. However, in relating the results to passport and other characterisation and evaluation descriptors, the observed patterns did not appear to be related to taxonomy or any other well known characteristics of groundnut.[PUBLICATION ABSTRACT]

Developing sorghum genotypes suited to post-rainy conditions requires understanding of the genetic diversity available for grain yield and its component traits. A multi-location trial was conducted at four experimental sites involving 97 post-rainy landraces and seven checks in an alpha lattice design with three replications during the post-rainy season of 2020–21 to understand the diversity of traits, genotype x environment interaction (G × E), and to select the landraces based on multi-traits mean performance and stability. Significant genetic differences were seen in the Indian post-rainy landraces for all the seven traits (days to 50% flowering, DFF; plant height, PH; panicle weight per plant, PWPP; grain yield per plant, GYPP; 100-grain weight, TW; seed hardness, SH and number of grains per panicle, SeedPP) studied. Trait heritability was moderate to high, indicating a good response to selection. Analysis of AMMI (additive main effects and multiplicative interaction) revealed significant G × E for all the seven traits. G × E contributed higher for GYPP, PH, PWPP, TW and SH than for DFF and SeedPP. Based on muti-traits mean performance and stability, multi-trait stability index (MTSI) identified 16 landraces of potential use. The results confirm existence of adequate trait diversity in the post-rainy landraces which may be exploited to develop genetically enhanced sorghum cultivars with higher productivity and better acceptance by stakeholders.

Given the wide use of the multi-climate model mean (MMM) for impact assessment studies, this work examines the fidelity of Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating the features of Indian summer monsoons as well as the post-rainy seasons for assessing the possible impacts of climate change on post-rainy season sorghum crop yields across India. The MMM simulations captured the spatial patterns and annual cycles of rainfall and surface air temperatures. However, bias was observed in the precipitation amounts and daily rainfall intensity. The trends in the simulations of MMM for both precipitation and temperatures were less satisfactory than the observed climate means. The Crop Environment Resource Synthesis (CERES)-sorghum model was used to estimate the potential impacts of future climate change on post-rainy season sorghum yield values. On average, post-rainy season sorghum yields are projected to vary between