In order to get an overview on the genetic relatedness of sorghum (Sorghum bicolor) landraces and cultivars grown in low-input conditions of small-scale farming systems, 46 sorghum accessions derived from Southern Africa were evaluated on the basis of amplified fragment length polymorphism (AFLPs), random amplified polymorphic DNAs (RAPDs) and simple sequence repeats (SSRs). By this approach all sorghum accessions were uniquely fingerprinted by all marker systems. Mean genetic similarity was estimated at 0.88 based on RAPDs, 0.85 using AFLPs and 0.31 based on SSRs. In addition to this, genetic distance based on SSR data was estimated at 57 according to a stepwise mutation model (Deltamu-SSR). All UPGMA-clusters showed a good fit to the similarity estimates (AFLPs: r = 0.92; RAPDs: r = 0.88; SSRs: r = 0.87; Deltamu-SSRs: r = 0.85). By UPGMA-clustering two main clusters were built on all marker systems comprising landraces on the one hand and newly developed varieties on the other hand. Further sub-groupings were not unequivocal. Genetic diversity (H, DI) was estimated on a similar level within landraces and breeding varieties. Comparing the three approaches to each other, RAPD and AFLP similarity indices were highly correlated (r = 0.81), while the Spearman's rank correlation coefficient between SSRs and AFLPs was r = 0.57 and r = 0.51 between RAPDs and SSRs. Applying a stepwise mutation model on the SSR data resulted in an intermediate correlation coefficient between Deltamu-SSRs and AFLPs (r = 0.66) and RAPDs ( r = 0.67), respectively, while SSRs and Deltamu-SSRs showed a lower correlation coefficient (r = 0.52). The highest bootstrap probabilities were found using AFLPs (56% on average) while SSR, Deltamu-SSR and RAPD-based similarity estimates had low mean bootstrap probabilities (24%, 27%, 30%, respectively). The coefficient of variation (CV) of the estimated genetic similarity decreased with an increasing number of bands and was lowest using AFLPs.

A broad scope of crop models with varying demands on data inputs is being used for several purposes, such as possible adaptation strategies to control climate change impacts on future crop production, management decisions, and adaptation policies. A constant challenge to crop model simulation, especially for future crop performance projections and impact studies under varied conditions, is the unavailability of reliable historical data for model calibrations. In some cases, available input data may not be in the quantity and quality needed to drive most crop models. Even when a suitable choice of a crop simulation model is selected, data limitations hamper some of the models’ effective role for projections. To date, no review has looked at factors inhibiting the effective use of crop simulation models and complementary sources for input data in South Africa. This review looked at the barriers to crop simulation, relevant sources from which input data for crop models can be sourced, and proposed a framework for collecting input data. Results showed that barriers to effective simulations exist because, in most instances, the input data, like climate, soil, farm management practices, and cultivar characteristics, were generally incomplete, poor in quality, and not easily accessible or usable. We advocate a hybrid approach for obtaining input data for model calibration and validation. Recommended methods depending on the intended outputs and end use of model results include remote sensing, field, and greenhouse experiments, secondary data, engaging with farmers to model actual on-farm conditions. Thus, employing more than one method of data collection for input data for models can reduce the challenges faced by crop modellers due to the unavailability of data. The future of modelling depends on the goodness and availability of the input data, the readiness of modellers to cooperate on modularity and standardization, and potential user groups’ ability to communicate.

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Drought is a major limitation to crop productivity worldwide. Plants lose most of their water through stomata, thus making stomata an important organ in the control of transpiration and photosynthesis. This study assessed the stomatal behavior of four cowpea genotypes grown under four moisture levels under hot semi-arid conditions. Stomatal conductance (gs) was measured at 47, 54, 70 and 77 days after planting (DAP). Biomass and carbon isotope composition (δ 13 C) were also determined at flowering. Genotype and moisture level significantly influenced gs. Genotypes varied in gs at vegetative stages (47 and 54 DAP) only. TVu4607 had higher gs under severe drought conditions at both 47 and 54 DAP. On the other hand, moisture level influenced gs at 54 and 70 DAP only. Stomatal conductance was severely restricted in cowpea under both moderate and severe drought conditions as gs was mostly below the threshold 0.10 mol m −2 s −1 . Relationships between: biomass and gs, and δ 13 C and gs were positive under severe drought only. The findings revealed that cowpea genotypes vary in gs under dry conditions and that the variation is more prominent at vegetative stage, suggesting that cowpea productivity in dry areas could be improved through selection of genotypes that maintain higher gs under dry conditions.

Lepidopterous stem borers seriously affect production of maize, Zea mays L., in sub-Saharan Africa. Intercropping maize with legumes such as lablab, Lablab purpurens (L.), is one of the effective systems to control stem borers. Sole culture maize and maize/lablab intercrop system of different lablab densities were planted at two locations to investigate the effects of intercrop system on incidence and severity of stem borers with particular reference to Chilo partellus (Swinhoe) (Lepidoptera: Pyralidae). Stem borer infestation was found to be more severe in sole culture maize than maize in maize/lablab intercrop. There was a significantly negative relationship between lablab densities and maize grain yields, suggesting a possible competition for resources between the two crops. It was concluded that density of lablab and date of planting of lablab in maize/lablab intercropping have significant affects on stem borer populations and maize grain yields.